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Record 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 on OpenAlexaffvenue
Scott Goldstein

Bibliographic record

VenueEvidence Based Library and Information Practice · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsMcGill University
Fundersnot available
KeywordsEthnic groupService (business)Communication sourcePopulationPersonalizationLibrary scienceWhite (mutation)MedicineMedical educationPsychologyWorld Wide WebSociologyComputer scienceDemographyBiology

Abstract

fetched live from 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.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.067
metaresearch head score (Gemma)0.253
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.354

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0670.253
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0030.008
Scholarly communication0.0050.005
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.136
GPT teacher head0.403
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2022
Admission routes2
Has abstractyes

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