Academic Librarian Search Committee Members Identify Inclusivity Concerns with On-Campus Interview Practices
Bibliographic record
Abstract
A Review of: Houk, K. & Neilson, J. (2023). Inclusive hiring in academic libraries: A qualitative analysis of attitudes and reflections of search committee members. College and Research Libraries, 84(4), 568-588. https://doi.org/10.5860/crl.84.4.568 Objective – To understand how academic librarian search committee members’ perceptions and attitudes affect the equitability and inclusiveness of the on-campus interview process. Design – Thematic text analysis of open-ended responses to short-answer questions from an online survey. Setting – Online survey conducted between February and March of 2021. Subjects – 166 academic librarians who had served on hiring committees for academic librarians in North America between 2016 and 2020. Methods – Participants for the 33-question survey were recruited through several academic library listservs and social media postings on Facebook, LinkedIn, and Twitter. The researchers first individually reviewed and coded all responses for short answer survey questions, then reviewed the codes together. Finally, a thematic map was developed after the researchers reached a consensus on their shared approach to coding and generating clusters of meanings. Main Results – Six major clusters were identified through thematic coding of participants’ text responses concerning their experiences of on-campus interview practices as hiring committee members. These themes represented challenges to the inclusiveness of academic librarian searches, and included search committees’ treatment of the interview process as either intentional or situational tests (1), reliance on the ambiguously defined selection criteria of fit (2), experience with varying levels of commitment to diversity, equity, inclusion, antiracism, and accessibility (DEIAA) values (3), frustration with prevalence of institutional bureaucracy throughout the hiring process (4), and uneven adoptions of inclusive hiring (5) or reflective practices (6). The researchers also noted a common respondent mistake of misinterpreting equal (i.e., identical) treatment of candidates as evidence of equitable interview practices. Conclusion – Findings from this study highlighted the importance of academic institutions and hiring committees adopting reflective practices to critically and intentionally incorporate DEIAA-informed practices in planning and conducting academic librarian searches. The authors also stressed the need to reduce possible biases in hiring practices favoring candidates who conforms to White, ableist, and heteronormative culture and values. Examples of these efforts included considering the necessity of each interview element for assessing candidate performances, proactively ensuring full accessibility of the interview itinerary, and operationalizing the definition of “fit” in assessing candidates’ abilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.104 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".