Thesaurus construction for community-centered metadata
Bibliographic record
Abstract
Community-engaged approaches to resource access require metadata practices that surface attributes relevant to local information needs and use terminology that reflects local language. This paper details the iterative and ongoing metadata work involved in facilitating access to aggregated items through the Downtown Eastside Research Access Portal. The challenges and strategies we describe here build upon and are relevant to knowledge organization projects seeking to repair issues of inaccurate and stigmatizing descriptive metadata for universal and local collections. After contextualizing the collection and the community, we describe our process in assessing areas of subject terminology in need of major repair, sources consulted for thesaurus terminology, and the approach we have taken to build a stand-alone thesaurus for this project, including our exploration and attempts at meaningful and respectful input into terms and term relationships.
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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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".