Indigenous knowledge in a post‐apology era: Steps toward healing and bridge building
Bibliographic record
Abstract
Abstract EDITOR'S SUMMARY An important aspect of ASIS&T's international outreach is service to indigenous populations, a need that has received greater recognition since Canadian Prime Minister Stephen Harper's public apology in 2008 for past disruption of Native families. The emerging field of indigenous librarianship can contribute significantly to the process of reconciliation in Canada and other countries with similar colonial legacies. Indigenous librarianship requires reconsidering the organization, classification and representation of library materials from a perspective free of culture‐ and language‐based assumptions. Key themes in indigenous librarianship include removing barriers to access, providing culturally relevant materials and services and departing from widely used knowledge organization systems such as the Dewey Decimal System to create classifications that reflect the Native worldview and epistemology. Successful examples include Australia's Pathways thesaurus project, the Māori Subject Headings from Aotearoa/New Zealand and the British Columbia First Nations Names Authority. Increased involvement by Indigenous people in information studies will enhance accurate representation of their cultures.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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, 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".