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Enregistrement W4220967896 · doi:10.18438/eblip30085

Uneven Adherence to Professional Guidelines and Potential Ethnic Bias in Service Provision Evidenced in Virtual Reference Service Interactions

2022· article· en· W4220967896 sur OpenAlexaffvenue
Scott Goldstein

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2022
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueLibrary Science and Administration
Établissements canadiensMcGill University
Organismes subventionnairesnon disponible
Mots-clésEthnic groupService (business)Communication sourcePopulationPersonalizationLibrary scienceWhite (mutation)MedicineMedical educationPsychologyWorld Wide WebSociologyComputer scienceDemographyBiology

Résumé

récupéré en direct d'OpenAlex

A Review of: Hamer, S. (2021). Colour blind: Investigating the racial bias of virtual reference services in English academic libraries. The Journal of Academic Librarianship, 47(5), 102416. https://doi.org/10.1016/j.acalib.2021.102416 Abstract Objective – To investigate whether there is evidence for implicit ethnic bias in virtual reference service interactions. Design – Email-based structured observation study. Setting – Academic libraries in England. Subjects – 158 email-based virtual reference service interactions from one of 24 academic libraries in England. Methods – The study used a sample of 24 academic libraries across eight of the nine regions of England (excluding London). The body of the email message sent to each library consisted of one of five questions and was identical except for personalization to the institution. The first three questions were designed to be more likely to be answered in response to an unaffiliated user, and the last two questions were designed to be less likely to be answered in response to such a user. Each library received an email with each question from a different sender during each of five weeks, plus a repeat of question one in week six with slightly altered wording to serve as a control question. Emails were sent on randomized work days at different times of day. The messages were signed with one of six names representing the largest distinct ethnic population groups in England and Wales: Hazel Oakland (White British), Natasza Sakowicz (White Other), Zhao Jinghua (North Asian), Priya Chakrabarti (South Asian), Ebunoluwa Nweke (Black African), and Aaliyah Hajjar (Arab). All names were feminine and represented unaffiliated users. Email replies were coded according to a set of 27 characteristics based on the two most well-known professional guidelines for providing best practice reference services, namely, IFLA and RUSA. Main Results – 133 out of 144 sent queries received a reply, of which 66 partially or fully answered the question. 158 total emails were received (since an email might receive multiple responses), and 67 of these partially or fully answered the question. Differences in how the librarian’s reply addressed the user were evident. Hazel was the only one never referred to by her full name, whereas Jinghua was the least likely to be referred to by her given name and most likely to be referred to by her full name or no name at all. Greeting phrases were used in most responses. About 20% of responses included a reiteration of the original request. Elements of the response which could be seen as promoting information literacy skills were provided in only 11% of responses. Natasza was the most likely to be referred to another source to answer her query, whereas Jinghua was least likely. Ebunoluwa was the least likely to receive a response to her query and least likely to have her question answered overall. Conclusion – The findings point to some evidence of unequal service provision based on unconscious bias. In the aggregate, Ebunoluwa received the lowest quality of service, while Jinghua received the highest. There were several instances of inappropriately addressing the user, or what the author refers to as name-based microaggressions, and this was most common for Jinghua. The likeliest explanation is that many librarians are unfamiliar with the ordering of names traditionally found in East Asian cultures. The most noticeable result of the study is an overall lack of consistent adherence to professional guidelines. For instance, most queries received a reply within a reasonable timeframe, and greeting and closing phrases were included almost universally. However, other elements of the author’s rubric, such as those corresponding to clarity and information literacy, were not consistently applied. The results point to a greater need for librarians to follow best practice in virtual reference services. Furthermore, the author believes that best-practice guidelines must actively engage with anti-racist ideas to address the issues that were found in the study.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,067
score de la tête « metaresearch » (Gemma)0,253
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,067
Score d'incertitude au seuil0,354

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0670,253
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0030,004
Études des sciences et des technologies0,0030,008
Communication savante0,0050,005
Science ouverte0,0020,004
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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 Opus0,136
Tête enseignante GPT0,403
Écart entre enseignants0,267 · 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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
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
Publié2022
Routes d'admission2
Résumé présentoui

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