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Enregistrement W4206421028 · doi:10.18438/eblip30035

Public Libraries Help Patrons of Color to Bridge the Digital Divide, but Barriers Remain

2021· article· en· W4206421028 sur OpenAlexvenueno aff
K. Roy MacKenzie

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2021
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueLibrary Science and Administration
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGrounded theoryDigital divideCritical race theoryDemographicsSociologyPsychologyLibrary scienceQualitative researchRace (biology)The InternetGender studiesSocial scienceComputer scienceWorld Wide Web

Résumé

récupéré en direct d'OpenAlex

A Review of: Pun, R. (2021). Understanding the roles of public libraries and digital exclusion through critical race theory: An exploratory study of people of color in California affected by the digital divide and the pandemic. Urban Library Journal, 26(2). https://academicworks.cuny.edu/ulj/vol26/iss2/1/ Abstract Objective – This study explored the role of the public library in the support of patrons of color who experience digital exclusion. Design – In-person and telephone interviews, grounded theory, and critical race theory. Setting – Public libraries in California. Subjects – Persons of color who were active public library technology resource users due to experiencing the digital divide. Methods – In-person, 60- to 90-minute interviews were conducted with participants referred to the author by public librarians at select libraries in California. Sixteen open-ended questions were asked, relating to demographics, access to technology at home, library technology access and use, technology skills, and thoughts on how libraries could change or improve technology services. A 20- to 30-minute follow-up interview was conducted during the phase of the Covid-19 pandemic when public libraries were closed. Interview transcripts were analyzed by the author, who created a codebook of common themes. Responses were analyzed through the lens of grounded theory and critical race theory. Main Results – Nine participants were recruited; six consented to the first interview and two of the six consented to the second interview. Four of the participants self-reported as Asian, one as Black/African American, and one as Hispanic/Latino American. None of the participants had internet access in their homes, though some reported having laptops or inconsistent cellular service. Common uses of library technology included job search activities (resume building, job searching, applications); schoolwork; research and skill development; and legal or housing form finding. Leisure activities including social media and YouTube were also mentioned. Access limitations included inconvenient library hours, particularly for those attending college or holding a job with daytime hours, and physical distance from the library. A common complaint was the time limit on computer access set by the library; “the concept of time” was mentioned “over 70 times collectively by all participants” (p. 14). Language was another barrier to access, mentioned by three of the participants. Most reported being more likely to ask for help from a library staff person who shared their language or had a similar background. Participants also reported wishing more technology workshops were offered, especially workshops in languages other than English. The two participants who took part in the second interview “expressed frustration and sadness” about the lack of library access during the Covid-19 pandemic (p. 16). One participant reported having to get internet access at her home for her children to attend school. The second participant expressed her difficulty in conducting research or printing information with only the small screen of her phone to provide access. Conclusion – Library patrons of color living within the digital divide make use of public library technology but experience multiple barriers. Libraries can alleviate these barriers by examining their hours, policies, and staffing models to be more accessible to patrons of color lacking internet access at home.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,011
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Communication savante
Catégories consensuellesCommunication savante
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,953
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,011
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0030,364
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,037
Tête enseignante GPT0,279
Écart entre enseignants0,242 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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