Putting the ‘I’ Back in BIPOC: Indigenous-Specific Inclusion Initiatives across Academic Libraries in the United States and Canada Dataset
Bibliographic record
Abstract
This dataset examines academic libraries’ resources and services specifically centering Indigenous patrons and Indigenous Knowledge, as well as targeted support for Critical Indigenous Studies (CIS) academic programs at colleges and universities across the United States and Canada. In addition to a comprehensive assessment of Indigenous-specific liaison librarians and libguides, this overview investigates correlations between additional overlapping factors including: Indigenous Studies curricular offerings, institutional land acknowledgements, research output, institutional distinction as Title III Minority Serving Institutions (MSI), and membership in collegiate associations such as the Association of American Universities (AAU), Association of Public and Land-grant Universities (APLU), and National Collegiate Athletics Association Division I Football Bowl Subdivision (NCAA FBS). As a result, this dataset provides multiple points of comparison which serves to outline a more thorough illustration of the academic environments in which college and university libraries operate. Moreover, this constellation of data points serves to demonstrate both the academic libraries concentrating their efforts to support Indigenous members of their respective campus communities, as well as how they align these efforts with broader campus initiatives.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.022 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.012 |
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".