The Five Labours of Equity, Diversity, Inclusion, and Anti-racism Work by Racialized Academic Librarians
Bibliographic record
Abstract
This study unpacks the experiences of academic librarians that identify as racialized to better understand the weight of equity, diversity, inclusion, and anti-racism work. The themes that emerged from the interviews with the librarians were emotional labour, interpretive labour, identity labour, racialized labour, and aspirational labour. These forms of labour are often oversimplified, unacknowledged, or unquantifiable. For one line on a curriculum vitae, committee, advisory, or working group related equity, diversity, inclusion, and anti-racism work may not be compensated or financially supported to reflect the intensity and expertise needed for the work. It is important to unpack the complexity of the work to demonstrate how to better support racialized librarians that engage with this work that contributes to changes in the academic library and profession.
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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.035 | 0.039 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| 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".