Equity, Diversity, and Inclusion in Institutional Research Data Management Strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.074 |
| Open science | 0.011 | 0.286 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it