LIBRARY ACCESS AND EQUITY FOR FIRST NATIONS, METIS AND INUIT
Bibliographic record
Abstract
The Canadian Library Association Code of Ethics includes the duty of librarians to provide equitable access to libraries for all users. In the case of the indigenous peoples of Canada, the First Nations, Métis, and Inuit (FNMI), we are not living up to our professional responsibilities. FNMI peoples do not use public and academic libraries in high numbers because of barriers to access and lack of equitable services. It is our responsibility as librarians to understand what barriers exist, and why, and to look for ways to eliminate them. Across North America this lack of access and the importance of equity in services has been well-documented (Bartleman, 2003, Bartleman, 2008, Burke, 2007, Lee, 2001, Sinclair-Sparvier, 2002). FNMI peoples do not use public or academic libraries as much as non-aboriginal people, although they do use and want band or tribal libraries (Burke, 2007, Lee, 2001, Lefebvre, 2003). This disproportionate use is a result of many factors: geographical proximity to a library, unaffordable user fees for off-reserve public libraries, collections that do not meet their needs, lack of participation in decision-making processes, lack of FNMI public and academic library staff, the perception that the library
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Other design | low |
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".