Heightened Awareness of Oppressive Systems May Impact Black Library Workers’ Perceptions of Inequitable Hiring and Retention Practices in Public and Academic Libraries
Bibliographic record
Abstract
A Review of: Caragher, K., & Bryant, T. (2023). Black and non-Black library workers’ perceptions of hiring, retention, and promotion racial equity practices. Journal of Library Administration, 63(2), 137-178. https://doi.org/10.1080/01930826.2022.2159239 Objective – To measure Black and non-Black library employees’ perceptions of their library’s efforts to hire, retain, and promote Black, Indigenous, and People of Color (BIPOC) library employees. Design – Literature review and subsequent questionnaire. Setting – Academic and public libraries in the United States and Canada. Subjects – 717 survey participants who met the criteria of working in the United States or Canada, and either being currently employed, retired, or unemployed library workers whose experiences are placed in an academic or public library. 68 subjects who selected Black as their ethnicity were coded separately from other ethnic designations. Methods – A joint effort of the Association of College and Research Libraries (ACRL), Association of Research Libraries (ARL), the American Library Association’s (ALA) Office for Diversity, Literacy and Outreach Services (ODLOS) and the Public Library Association (PLA) launched the Building Cultural Proficiencies for Racial Equity Framework Task Force in 2019. A subset of this task force broke out to create a survey titled Racial Equity in Libraries. A three-part survey was devised, covering demographics, personal experiences with racial equity, and workplace experiences with racial equity. The task force used non-probability convenience sampling and distributed the survey to several library listservs across the United States and Canada. Quantitative results underwent descriptive statistics; qualitative results underwent iterative thematic analysis. Main Results – Black participants made up 68 (9.5%) of all responses. Five qualitative themes emerged: unsuccessful hiring searches; acknowledgement that hiring of BIPOC is an ongoing issue; no BIPOC employees; organization-based issues impacting hiring; and hostile work environments for BIPOC. Conclusion – Black participants were more likely to report that their library hires, promotes, and retains BIPOC library workers compared to non-Black participants. However, Black participants were also more likely to refute that their employers were making efforts to hire, retain, and promote BIPOC library workers than their non-Black counterparts. This may be due to Black participants' greater sense of awareness of oppressive systems surrounding them.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".