Inclusion and identification of locally-authored items in library collections
Bibliographic record
Abstract
This research explores how public libraries support local authors, with a focus on if and how these works are included in library collections and made findable to patrons. Twelve public libraries, four each from British Columbia, Alberta, and Saskatchewan, were selected to analyze collection development policies and item metadata. Qualitative content analysis was used to code collection policies, and systemic analysis of item record metadata was used to understand methods of identifying locally-authored items. The results of this research indicate that collection policies provide both opportunities and barriers for acquisition of locally-authored items, including those items that are self-published. There is a lack of consistent methods for identifying items as locally-authored within item metadata. This research discusses some of the challenges associated with identifying items as locally-authored, and concludes with recommendations for modifying collection policies and methods for identifying items in order to make locally-authored items more accessible and discoverable to the local community.
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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 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".