Equity, Diversity, and Inclusion in Institutional Research Data Management Strategies
Bibliographic record
Abstract
Research data management (RDM) is a field of emerging concern for academic librarians. As funder agencies increasingly mandate institutions and researchers to ethically and responsibly manage their research data, academic librarians are frequently tasked with creating institutional strategies and services to support researchers. This article explores how a racialized librarian at a medium-sized, teaching-focused Canadian university created an institutional research data management strategy through a process informed by critical librarianship research and contributive justice (Gomberg, 2016; Honma & Chu, 2018). It examines the lack of equity, diversity, and inclusion (EDI) principles in both funder directives and RDM research literature and proposes an approach to do institutional RDM work in an EDI-centered way.
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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 | 0.324 | 0.261 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.032 | 0.091 |
| Scholarly communication | 0.045 | 0.040 |
| Open science | 0.005 | 0.054 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".