Inching Forward in the Face of Hegemonic Factors: Examining Metadata Contradictions Across University Indigenous Collections
Bibliographic record
Abstract
Changes to terminology take time and heighten tensions in language description, in preference of naming conventions, and institutional practices. External forces like the mandates of the United Nations Declaration on Indigenous Peoples, and the Canadian Federation of Library Associations (CFLA) recommendations for the Truth and Reconciliation Calls to Action, influence the actions taken by organizations and move us forward. However, other structural and systemic forces can impede these efforts. At the University of Calgary, decisions about which vocabularies to use are further muddied by different practices across our units, and the methods available to make updates to our systems. Our Library Managment System (LMS) needs to wait for updates from the vocabulary authorities, while our digital collections does not, allowing them to make big changes faster. By examining the applicable vocabularies in Canada, we can surface the hegemonic forces at work, that exist internal and external to the institution. For instance, while standardization aids in discovery, it also drives a hegemonic use of language which does not describe Canadian content such as Indigenous names. In grappling with these forces, we confront and oppose them as we work through the process of updating subject headings and descriptive language for Indigenous content within our systems.
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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 | 0.035 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.043 | 0.053 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 0.005 |
| 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".