Overview

This chapter focuses on descriptions of creators, contributors, depicted people, audience, and more people or groups. This type of information has tended not to be explicit in library metadata. Furthermore, this type of information may have been, and sometimes still can be, inaccurate or biased. This chapter builds on Chapter 1: Inclusive description, focusing on structured metadata rather than variable text fields to provide information about characteristics or attributes of people or groups. This chapter also focuses on library resources, digital objects and collections, and name authorities, but it is worth noting that archives metadata work has often included creator information in some way, often through recording provenance or creating archival authority records.^[1]

Information entailing characteristics or attributes of these people and groups may be available indirectly in library catalogs and collection discovery portals. For example, the name of the series, the digital collection title or summary, descriptions in summaries or notes, and narrow subject terms displayed for titles or digital objects may provide ways to glean characteristics of the creators or other connected people and groups. It is unfortunate that, unless one is looking at a specialized collection consisting of materials focusing on a minoritized group, like a LGBTQIA+ library or a collection of oral histories about women in STEM, the diversity of creators and contributors usually cannot be discoverable through our library resources and collections.^[2] Searching for library materials written by a scientist who is a woman and a person of color, for example, is difficult unless you know their name.

In the past, well-meaning metadata creators have made efforts to describe these people but made erroneous assumptions or imposed biases (implicit or otherwise) in the metadata descriptions.^[3] Conscious editing (explained in Chapter 1) has gained strong momentum among metadata creators in recent years, but many challenges remain, including limited vocabulary options and limited availability for metadata elements to encode person and group attributes. The current political climate means there may be pushback—for example, critics may object to library metadata highlighting minoritized group creators over others. Nevertheless, we can and should take the time to enhance metadata describing our library collections and resources with attributes for people and groups. We encourage metadata creators to include person and group attributes when appropriate. Such attributes improve discovery and spotlight materials created by or about historically overlooked groups. At the same time, it is important to consider and balance factors such as labor, need, and privacy. The Program for Cooperative Cataloging, in a recent position statement, reminds Name Authority Cooperative (NACO) participants that our goals as metadata professionals include facilitating discovery and that we should be judicious about including personal data in name authorities.^[4]

Potential prompts

Just because we can include descriptions for creator or contributor characteristics does not mean we do this in every case. This work is time-intensive, as we would want to seek and use accurate information and do our best to select appropriate terms or languages that respect the person or group. There are various ways we might be prompted to consider creating metadata describing the characteristics of the creators, contributors, depicted people or groups, or audiences.

Often, when working on materials, we may notice that a book is written by someone who might identify as a marginalized community member, or that the collection being digitized is about a historically overlooked group. Cues often come from author photographs or the names on materials, but take care to investigate what captured your attention! Implicit bias is interwoven into our daily lives, but we can consciously work to avoid imposing these biases in metadata work. The advice provided in Chapter 1 may be helpful here.

Perhaps a selector in your library has forwarded a title with the request to enhance the bibliographic description for diversity, equity, and inclusion (DEI). This could happen for any reason; they might have observed a cue as described above, or they might have personal knowledge about the author because they are a faculty member or a local author, recently visited the campus to give a talk, were recognized for an award, or were featured on a local news program.

No matter how a candidate for DEI metadata enhancement has caught your attention, take a few minutes to consider whether the added time to research the author or creator is something worth pursuing. Determine and adhere to the amount of time permissible for due process that works for you and your team–perhaps 15 minutes. Review these questions before proceeding:

• Can you confirm the public identity of the person or group with accurate information?
• Would it be helpful to provide additional information about the person or group?
• Would including the information about the person or group violate their privacy?^[5]

Taking the time to consider these questions will help you make an informed decision and permit you to proceed with providing information about the person or group now, track it for later work, or take no further action to include attributes for them in the metadata. Keep in mind, if you can’t find any useful information within a short time frame, you can try again another day or pass it along to a colleague for a fresh look. See Workload Management in Chapter 1 for more tips.

Considerations

It is crucial not to base your description of the creator on how they appear in a photograph without additional source information. Similarly, do not rely only on their names or appearance to determine their gender, race, ethnicity, or nationality. Numerous studies highlight the errors of our past practices using this approach.^[6]

First and foremost, review the source(s) of information for the person or group you are considering describing. A main consideration is the person or group’s self-identification, which we should respect.^[7] Is the information public and does it appear factually accurate? Are you looking at the book jacket or cover, a biography included in the book, the author’s personal website or social media posts, or a news interview with them? The publishers may include a biography, or the creator may have shared their own biography, on their websites. If the person or group has an active public presence, consider also reaching out directly through email, social media, or phone. Archives can be a valuable resource with manuscript collections or records related to the person or group, but do confirm whether these contain any kind of privacy embargoes on identifying information.

Even when a request for DEI metadata enhancement comes from a colleague who may know an author personally or saw them speak on campus or presented on a news show, take a few minutes to seek out firsthand sources to verify the information. It is essential to be consistent and verify the information about persons or groups with written, factual sources while creating the metadata.

Evaluating sources

When reviewing sources, keep in mind that, as metadata professionals, we are not interested in outing persons or groups, or violating their privacy, no matter their viewpoints. It would be unethical to use or cite information from sources where stolen or hacked personal, sensitive information is posted (e.g., on doxing websites or social media posts)^[8] in our metadata.^[9] If you happen across a webpage that lists a person’s residential address, personal phone number or email, or other sensitive information, this is a good indicator that the website is the type of source to avoid as unreliable or malicious.

We tend to start our online research with search engines that often bring us to webpages or online articles that can be taken out of context very easily. When these excerpts are available on familiar websites like Wikipedia and mainstream newspapers, it is understandable to accept these as safe sources, but try to quickly review the Wikipedia article’s citations or the list of publications by the journalist to get a sense of their reliability.

Many information professionals are researchers at heart. We might come across interesting facts about the persons and groups we are describing, but it is not necessary to include every tidbit we find in our metadata or name authority records.^[10]

Removing information

Occasionally, a person or a group’s representative may request that information about them be removed. Or in your searches, you may come across cases where they clearly indicate a preference against recording identity information in library catalogs or digital collections. Such requests should always be respected. Removal notices may be tracked without violating privacy, to help prevent metadata professionals from inadvertently inserting the unwanted identity-related details again.

General instructions regarding confidential or removed information may be retained with a donor file in the archives. In the case of name authorities, a note indicating the entity’s request or preference can be recorded without specific details in a source data found field (MARC 670), as shown in Figures 2.1 and 2.2 in Chapter 2. Some digital collection platforms can have private note fields or internal changelogs available that help staff track these decisions or updates. To our knowledge, this type of information is not commonly recorded in bibliographic records, yet there is sufficient justification for doing so. It is feasible for staff to add local, private notes in bibliographic metadata to indicate decisions to remove or not to record identity information for a person or group (without recording the omitted details). As a community, we have yet to arrive at a best practice for updating shared records (such as in WorldCat), but possible MARC fields for this use include 500 for a general note (as illustrated in Figure 1.2b); 588, used to note source of description, which often contains information helpful to catalogers; and 583 or action note, which may be useful for recording description decisions.^[11]

Vocabulary options

For simplicity and considering today’s online environment, it may make sense to use faceted subject terms to describe people and groups instead of complex pre-coordinated headings or terms that are outdated or combine attribute groups into one without providing comprehensive options.^[12] Ideally, users searching for Black woman scientists, for example, could customize a search with a facet for author attributes by selecting terms to represent Black people, Women, and Scientists.

Library of Congress Subject Headings (LCSH), as many critics have shown,^[13] is problematic because it does not account for all possible groups,^[14] and it even emphasizes gender in unnecessary and incomplete ways. Markedness, from linguistics, identifies something as atypical, “other,” or different.^[15] LCSH terms have many instances of markedness, such as Doctors versus Women doctors, or Librarians versus Women librarians.^[16] Markedness makes power visible by means of the normalized, unmarked term. It does not seem a good idea to use Women librarians when LCSH does not provide an equivalent for all gender identities. Also consider carefully the merit of including an attribute related to the gender demographic group category, as discussed in this and the preceding chapters.

Metadata creators may find it easier to use the faceted approach because pre-coordinated subject terms for persons and groups would require planning and identification for the fullest, most accurate term to apply. Despite markedness and other issues, some existing LCSH terms may be sufficient, but we can use any controlled vocabulary that best suits our needs. The Library of Congress has begun its vocabulary on Demographic Group Terms (LCDGT) (http://id.loc.gov/authorities/demographicTerms), which is growing but by no means comprehensive. Published online through the Library of Congress’s Linked ID Service, LCDGT is searchable and includes multiple serializations and bulk download options. Each term displays a label, description, URI, demographic group category, sources, variants, and version history. LCDGT includes several demographic group categories: Age; Educational level; Ethnic/cultural; Language; Medical, psychological, and disability; National/regional; Occupation/field of activity; Religion; and Social. With multiple group categories, this means there may be several terms to review for selection. Look at each term for the category that best meets your needs. Also be mindful that terms may not be available across all the categories. For example, searching LCDGT for potential terms for a Hmong author gives Hmong Americans,^[17] Hmong (Asian people),^[18] Hmong speakers,^[19] and a few other possibilities. The first two terms are both in the Ethnic/cultural category; either one could be included as appropriate, after confirming whether the author in question is American. The last term, Hmong speakers, is in the Language category and could be good to include when the resource focuses on Hmong language. Other vocabularies that may be suited to describing persons and groups include but are not limited to: Homosaurus, FAST, AAT, AFSET, and ERIC.

Homosaurus (https://homosaurus.org/) is a linked data vocabulary that focuses on LGBTQIA+ terms.^[20] The full vocabulary list is browsable online; furthermore, the vocabulary is available in several serializations for incorporating into systems.^[21] For each term, this vocabulary includes an identifier, preferred term, alternative term (use for), scope note, versioning information in dates and term replacements, broader and narrower terms, and a hierarchical display. Homosaurus contains many specific terms suitable for use for demographic groups, such as Afro-Latin American LGBTQ+ people.^[22] Not all libraries need this level of specificity; however, this vocabulary, built through support and feedback from the LGBTQIA+ community, is considered authoritative. It is a good option to incorporate when your user community interests require this high level of specificity.

OCLC’s Faceted Application of Subject Terminology (FAST) (https://fast.oclc.org/searchfast/) vocabulary is based on LCSH, so some of the issues mentioned above (markedness or harmful preferred terms) also exist in FAST. Terms should be reviewed before selection and use. An example of this is the soon-to-be updated headings related to Deaf people.^[23] For example, in the heading Deaf,^[24] which LCSH and FAST preferred over Deaf people, the omission of the word “people” de-humanizes them. Hearing impaired is a catchall term that might be used to describe persons and groups with hearing loss;^[25] however, the alternative term lacks the word “people” and is an outmoded name for deaf and hard-of-hearing people. Despite these problems, FAST may still be useful in some cases because it is easier for people to learn and apply. FAST is searchable, and several integrations and serializations are available. Term display includes identifiers, alternative headings, usage statistics from the Library of Congress’s database and WorldCat, and sources.

The Art & Architecture Thesaurus Online (AAT) (https://www.getty.edu/research/tools/vocabularies/aat/) from the Getty Research Institute is a robust generic vocabulary that includes some terms that can be used as demographic group terms.^[26] AAT can be searched and browsed online. This vocabulary provides many serializations of its data for use in other systems. Each term includes an identifier, alternative terms in English and other languages when available, hierarchy, and sources/contributors. Review possible terms in AAT carefully because they may not reflect current terminology used by the people they are describing.

The American Folklore Society (AFS) publishes its Ethnographic Thesaurus (AFSET) (https://id.loc.gov/vocabulary/ethnographicTerms.html) in the Library of Congress’s Linked Id Service.^[27] Some of the terms may be used as demographic group terms, such as Deaf persons and Non-binary people.^[28] Like LCDGT, AFSET can be searched, and the term information includes label, description, URI, serializations, sources, variants, and version history. With AFSET’s focus on ethnographic matters, the terms themselves appear current, respectful, and appropriate; however, not many ethnic groups or nationalities are represented.

The Institute of Education Sciences database, Educational Resources Information Center (ERIC) (https://eric.ed.gov/), also maintains a list of descriptors (subject-related terms) for use within ERIC, with some options for describing attributes of persons and groups. The descriptors tend to be broad; for example, it provides the term Hmong people but does not give options for Hmong Americans.^[29] Nevertheless, the ERIC thesaurus (https://eric.ed.gov/?ti=all) may be useful for curricular and education-focused collections. The thesaurus is searchable online (select the Thesaurus tab), and term display includes scope notes, categories, broader terms, narrower terms, related terms, and alternative (use for) terms.

Personal and group attributes in resource description

MARC

In 2013, new MARC fields were added to provide metadata creators opportunities to describe characteristics of audiences (MARC 21 field 385) and creators/contributors (386).^[30] Field 385, for audience characteristics, allows metadata creators to record characteristics pertaining to the intended audience of the work described. When working on a record describing a book written for nursing students, the metadata creator would identify and select, if possible, at least one term that represents the audience group, such as Nursing students.^[31] Likewise, 386, for creator and contributor characteristics, may be used by metadata creators to record characteristics related to the people (or group) responsible for the intellectual content of the work being described. In the same nursing book from above, if the author biography provided clear information that the author was a Black nurse, two demographic group terms to consider including might be Black people and Nurses from LCDGT.^[32]

In general, either the demographic group term or the demographic group code should be included in 385 and 386 as non-repeatable subfields m ($m) and n ($n), respectively. Here, demographic group terms actually refer to the category groups, not the vocabulary terms or headings themselves. The demographic group code is the MARC code equivalent for a demographic group term (category). So, for the Ethnic/cultural demographic group category, the corresponding code is “eth.”^[33]

Individual vocabulary terms or headings corresponding to creator or audience characteristics can be placed in subfield a ($a) and may be combined with a demographic group code from the same vocabulary. Characteristic terms in subfield a ($a) can be repeated in the same field if they are from the same selected vocabulary, and the source vocabulary code should be cited in subfield 2 ($2). 385 and 386 can be repeated as necessary to incorporate appropriate characteristics from different vocabularies.

Both fields include subfield encoding for authority identifiers, source vocabulary codes, specified materials, and linkages. After 2013, these fields were expanded with additional subfields to provide the ability to encode URIs, data provenance, and in the case of 386, relationship information. Not all subfields for 385 and 386 are covered in detail here because they are universally applied in multiple MARC fields.^[34] After the 2013 release, these fields were slow to be adopted and populated in MARC records, but they are much more commonly found now.

Figures 3.1 through 3.3 are example snippets of records highlighting how characteristics for audience (385) or creators/contributors (386) may be included. Although 385 and 386 are repeatable as long as the headings are sourced from the same vocabulary, many people choose to separate them for readability or for easier parsing in library discovery environments. Including the demographic group codes could improve the granularity of filtering options and help users differentiate between similar terms. Another reason to repeat the fields is shown in Figure 3.1; the demographic group categories (386$n) are different for each.

Figures 3.2 and 3.3 contain audience characteristics using controlled vocabulary terms. Providing audience characteristics information in 385 fields may be helpful because not all library discovery interfaces make searchable or display the fixed field (MARC LDR 008 position) for audience or variable-text information included in the summary note (MARC 520) or target audience note (MARC 521). In Figure 3.2, one LCDGT has been added to describe the Age group category. The textbook example in Figure 3.3 is more challenging; there are no apparent vocabularies to provide headings for Veterinary technicians or Veterinary nurses. If these Occupational/field of activity (code: occ) demographic groups would benefit your library users in addition to Veterinary students,^[35] available in LCSH, it would be acceptable to add them to the library’s local record versions, accompanied with a “$2 local” notation. The code soc, for Social group category, accompanies Veterinary students. For consistency and interoperability, it is acceptable to follow the demographic group categories and codes established in LCDGT, and these established in LCDGT, and these have been included in Figure 3.3 even though none of the terms came from LCDGT.^[36]

Beyond MARC

Not many metadata schemas include elements for describing persons or groups, much less attributes/properties. The Bibliographic Framework (BIBFRAME) designed to replace MARC is expressed in Resource Description Framework (RDF). RDF is the standard data model for describing and exchanging information in the semantic web environment.^[37] BIBFRAME’s origins as a replacement for MARC is evident in its many elements and attributes that correspond to MARC encoding. BIBFRAME class creatorCharacteristic is one place to record the URI value for a term describing one characteristic of the creator or contributor.^[38] Figure 3.4 gives an example of one creatorCharacteristic for Chanda Prescod-Weinstein as a URI triple without any notation syntax. creatorCharacteristic can be repeated.

Figure 3.4 is a BIBFRAME snippet example presented as a triple. The URI for Prescod-Weinstein’s authorized access point from the Library of Congress Name Authority File (LCNAF) is the subject,^[39] shown on the first line. The URI for creatorCharacteristic is the second line, or predicate. The object in the last line is the URI for LCDGT term Physicists.^[40] Additional triples would describe other characteristics for Prescod-Weinstein.

<http://id.loc.gov/authorities/names/n2020051916>
     <http://id.loc.gov/ontologies/bflc/Creator Characteristic>
          <http://id.loc.gov/authorities/demographicTerms/
            dg2015060362>

The CIDOC Conceptual Reference Model (CIDOC CRM) is another metadata option where it is possible to describe person and group attributes.^[41] This international standard was originally issued by the CIDOC Documentation Standards Group, under the International Council of Museums’ International Committee for Documentation. CIDOC CRM is used in the cultural heritage and museum community.^[42]

The E39 Actor entity in CIDOC-CRM contains two subclasses,^[43] E21 Person,^[44] and E74 Group.^[45] E39 Actor has the direct incoming property P107 has current or former member (is current or former member of) through its E74 Group entity subclass.^[46] This property works bidirectionally; the first phrasing applies when encoding E74 Group and the other (parenthetical version) when encoding E39 Actor. Because it is a subclass of E39 Actor, E21 Person can inherit related P107 properties.

<E39.Actor>
     <Identifier>
          Prescod-Weinstein, Chanda
     </Identifier>
     <P107.is_current or former member of>
          <Identifier>
               Physicists
          </Identifier>
          <in_class>
               E74.Group
          </in_class>
     </P107.is_current or former member of>
</E39.Actor>

Figure 3.5 presents an example snippet expressed in CIDOC-CRM describing Prescod-Weinstein with Physicists as a group attribute.

The Metadata Object Description Schema (MODS) from the Library of Congress provides a few subelements for the name element.^[47] The description subelement is a variable text field where more information can be included about the named entity, and it is a suitable place to include attributes for the person or group.^[48] For an example of how we might use these elements in MODS to enhance descriptions for persons and groups, see the last example at the end of this chapter (Figure 3.11).

Personal and group attributes in name authorities

In name authority records, a few options are available for recording characteristics of the person or group. Be aware of position statements or reports from PCC against including sensitive data, including gender, in name authority records.^[49] Chapter 2 goes into more detail about names. Most of our information systems do not cross-reference information stored in name authority records beyond variant headings listed in see from tracings (MARC fields 4XX) and see also from tracings (MARC fields 5XX).^[50] The primary purpose of name authority records is to disambiguate names, and it is only necessary to include enough information for metadata professionals to ascertain which authority record matches the person or group in question. People or groups with similar names may have different occupations and other characteristics that can be included in the record. The considerations reviewed earlier in this chapter also apply when creating or enhancing authority records.

In MARC authority record format, possible fields to record characteristics of the person or group include other attributes of person or corporate body (field 368) and occupation (field 374).^[51] For other attributes of person or corporate body (368), subfields a (Type of corporate body) and c (Other designation) are most relevant for persons and groups. 368$c has been used to note demographic group terms indicating ethnicity in some name authority records. Another MARC field (375) is available to record gender, but as mentioned in Chapter 2, it is not recommended for use.^[52]

When recording other attributes of a person or corporate body (in MARC field 368) or the occupation (MARC 374), metadata professionals may select a term and encode it as follows. The term is placed in the appropriate subfields for type of corporate body (368$a), other designation related to person or corporate body (368$c), or occupation (374$a), and the code corresponding to the source vocabulary is included in subfield 2 ($2). Multiple terms can be repeated, with additional subfields in the same field unless they come from different vocabularies. In the latter case, a new field for each vocabulary is necessary. Figure 3.6 shows the 374 field included in Prescod-Weinstein’s Library of Congress Name Authority File (LCNAF) record.

Examples

Our colleagues at Iowa State University Library conducted a research project to search for, identify, and collocate STEM resources created by people from minoritized groups. Their introductory text echoes sentiments expressed above and in other chapters:

[W]hile there is increasing interest in these types of works they remain difficult to find, recommend, and purchase. This is frustrating for educators and librarians, but especially for readers who want to see diverse experiences and cultures reflected in media and education.^[53]

Their resulting data set is available in their Diverse STEM Reading: A Layer Cake of Problems bibliography.

Using a selected work, Black Software by Charlton D. McIlwain, from the Layer Cake data set, various team members created the metadata examples below. At the publisher’s website, the book synopsis indicates the content focuses on African Americans.^[54] A wedding announcement in the New York Times shows that McIlwain was born to American parents and, therefore, would have American nationality. McIlwain is also identified in several online biographies as the founder of the Center for Critical Race and Digital Studies,^[55] a research center for scholars of color.^[56] Multiple sources also list McIlwain as a faculty member at New York University.^[57] Throughout these resources, he, his, and him pronouns are used in reference to McIlwain. With this research, we are comfortable inferring that we may elect to include certain vocabulary terms to describe McIlwain, such as Americans, African Americans, Men, and/or University and college faculty members from LCGDT.^[58]

The following examples are brief and generally contain corresponding elements to encode the book title, the author’s name, and at least one LCDGT term to provide an author attribute. These examples are sufficient to demonstrate how metadata creators might consider aiding discoverability of works through including attributes for people and groups in a few metadata schemas commonly used by information professionals.

Figure 3.7 shows the creator/contributor characteristics (MARC 386) fields listed in the MARC record for Black Software. For guidance on how to encode 386 fields with LCDGT, or your preferred vocabulary terms, refer to the earlier part of this chapter.

And Figure 3.8 provides an example of how McIlwain’s LCNAF record has been enhanced by adding a term for other attributes of person…other designation (368$c) and the occupation in MARC field 374 as described in the previous section.

Two BIBFRAME examples are provided in Figures 3.9 and 3.10; both are expressed in Turtle (Terse RDF Triple Language or TTL). The first one uses Dublin Core Metadata Initiative Metadata Terms (dcterms), and the other, Metadata Authority Description Schema in RDF (MADS).

@prefix dcterms: <http://purl.org/dc/terms/>.
@prefix bflc: <http://id.loc.gov/ontologies/bflc/>.
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>.
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>.

<http://www.example.com/books/1>
     dcterms:title "Black software : the Internet and racial 
       justice, from the AfroNet to Black Lives Matter" @en;
     dcterms:creator <http://id.loc.gov/authorities/names/
       n2003110343>.

<http://id.loc.gov/authorities/names/n2003110343>
     relators:aut <http://www.example.com/books/1>;
     bflc:creatorCharacteristic <http://id.loc.gov/authorities/
       demographicTerms/dg2015060362>.

The dcterms and BIBFRAME example (Figure 3.9) contains two stanzas with abbreviated notation. The first stanza describes the work through two triples with the same subject, which is an imaginary URI intended to represent the work. The first triple’s predicate consists of a dcterms title element with an object consisting of a literal text string for the full book title. The second triple in this stanza consists of a dcterms contributor predicate with the URI for Charlton D. McIlwain’s Library of Congress Name Authority File access point as the object.

The second stanza describes Charlton D. McIlwain, using the author’s name authority URI as the subject. The first predicate, “is author of” ( relators:aut ), has the imaginary URI for the work as its object. The next predicate, BIBFRAME creatorCharacteristic, has as its object the URI for the LCDGT term African Americans.

@prefix bf: <http://id.loc.gov/ontologies/bibframe/>.
@prefix bflc: <http://id.loc.gov/ontologies/bflc/>.
@prefix madsrdf: <http://www.loc.gov/mads/rdf/v1#>.
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>.
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>.
@prefix datatypes: <http://id.loc.gov/datatypes/>.
@prefix lclocal: <http://id.loc.gov/ontologies/lclocal/>.

_:b0_b4 a bf:Title;
     rdfs:label "Black software : the Internet and racial justice,
       from the AfroNet to Black Lives Matter";
     bf:mainTitle "Black software : the Internet and racial 
       justice, from the AfroNet to Black Lives Matter".

<http://id.loc.gov/rwo/agents/n2003110343> a
     madsrdf:PersonalName, bf:Agent;
     rdfs:label "McIlwain, Charlton D., 1971-";
     madsrdf:isIdentifiedByAuthority <http://id.loc.gov/
       authorities/names/n2003110343>;
     bflc:creatorCharacteristic <http://id.loc.gov/authorities/
       demographicTerms/dg2015060362>.

<?xml version='1.0' encoding='UTF-8'?>

<mods xmlns:xlink="http://www.w3.org/1999/xlink" version="3.7" 
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" 
xmlns="http://www.loc.gov/mods/v3" xsi:schemaLocation=
"http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/
v3/mods-3-7.xsd">

     <titleInfo>
          <title>
               Black software
         </title>
         <subTitle>
              the Internet and racial justice, from the AfroNet 
                to Black Lives Matter
         </subTitle>
     </titleInfo>
     <name
        type="personal"
        authority="naf"
        authorityURI="https://id.loc.gov/authorities/names"
        valueURI=”http://id.loc.gov/authorities/names/
        n2003110343”>
          <namePart>
               McIlwain, Charlton D., 1971-
          </namePart>
          <role>
               <roleTerm type="code">
                    aut
               </roleTerm>
          </role>
          <description>
               African Americans
          </description>
     </name>
</mods>

A last example, Figure 3.11, is a simple record encoded in Metadata Object Description Schema (MODS). Note that in the MODS record, a description element is included in the overarching name metadata as a means to provide additional information about the person. The LCDGT term label itself is used in description rather than the URI.

Conclusion

In all, describing people and groups is a developing area of metadata work, particularly in machine operable and interoperable ways. It has been common to describe people depicted in photographs, but previous efforts may have caused harm or been non-inclusive. These descriptions of persons and groups were generally limited to human-readable text summaries or notes, which is covered in Chapter 1, but we now have machine readable and interoperable metadata encoding options available. We can use these metadata elements along with critical selection of corresponding vocabulary terms that identify attributes of the authors, contributors, audiences, and other related persons and groups in our library resources and collections. Readers may refer to Chapter 5 for additional considerations regarding selecting and assigning subject headings.