Decolonizing Description: First Steps to Cataloguing with Indigenous Syllabics
Bibliographic record
Abstract
In light of the TRC Calls to Action from 2015 and the CFLA’s Truth & Reconciliation Report and Recommendations from 2017, many libraries in what is known as Canada have begun to take steps towards decolonization. Decolonizing bibliographic descriptions in library catalogues is an important part of this process, as this can impact both the ability to access Indigenous materials and the representation of Indigenous Peoples and Knowledges in the library. While various efforts to work towards accurately and respectfully representing Indigenous Peoples and Knowledges in library catalogues are ongoing, the inclusion of Indigenous Syllabics in bibliographic records is one way in which cataloguers can begin to put these efforts into action. In addition to collaborating with Indigenous community members and Indigenous librarians on this work, there are a variety of resources and tools available online that can aid cataloguers in creating accurate and culturally appropriate descriptions of Indigenous materials. This extended abstract provides context and information that is central to this work, and gives a cursory overview of how one might insert Indigenous Syllabics into bibliographic records.
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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 | 0.025 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.017 |
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".