Resources on the Net: Current Challenges to Public Education and Public Libraries and Responses from Academic Libraries
Bibliographic record
Abstract
The idea for the present theme originated from an upcoming Canadian Association of University Teachers (CAUT) Librarians Conference called Contested Terrain: Shaping the Future of Academic Librarianship. The conference, to be held in Ottawa on October 26 and 27, 2012, will look at ways that academic libraries can withstand and fight back against efforts to marginalize our profession and devalue our libraries. This review of recently published web literature begins with a look at the underlying issues as to why privatization has become a burning issue in both the public education system and public libraries. There are lessons to be learned here by academic libraries that are now faced with similar challenges. Within this climate of uncertainty, academic libraries have begun to respond by demonstrating value to their stakeholders. [...]
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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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.037 | 0.027 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 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".