Bibliographic record
Abstract
In 2020 we have experienced movements to support local creators, restaurants, and businesses; how can the library community support local authors? This extended abstract discusses research conducted about how public libraries support local authors, with a focus on how these works are included in library collections and made findable to community members. Twelve public libraries from British Columbia, Alberta, and Saskatchewan were selected for analysis of collection policies and item metadata. Qualitative content analysis is used to code collection policies, and systemic analysis of item metadata is used to understand methods of identifying locally-authored items. The results of this research indicate that collection policies provide both opportunities and barriers for including locally-authored items and there is a lack of consistent methods for identifying items as locally-authored within item metadata. Some of these barriers can be attributed to the challenge of identifying and defining “local authorship”. This extended abstract will conclude with recommendations for how libraries can modify collection policies and methods of identifying items as locally-authored in order to support local authors and make these items more accessible to the community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.006 |
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".