The Impact of the Prince Edward Island School Library Policy on the Development of School Library Programs Across Prince Edward Island
Bibliographic record
Abstract
In Canada's smallest province, Prince Edward Island (PEI), the PEI School Library Policy and its supporting documents have guided the development of school library programs since 1989. With a great number of educational changes as a back-drop, this study explored the impact that policy has had on school library programs in PEI. The researchers designed a comprehensive Survey of School Library Resource Centres that probed five key areas: facilities, resources, personnel, programs and services in the 66 schools across PEI. Following the completion of the Survey, 48 principals and teacher-librarians were interviewed for an hour each. 64 of the 66 schools took part and the Survey data and the interview data were analyzed using exploratory data analysis that resulted in dozens of visual and numerical summaries. Data was reported under each of the five major areas probed and 25 Actions were recommended. The study yielded specific detail on the status of school library facilities and resources, and provided a comprehensive look at staffing procedures and problems. The educational concepts of resource-based learning, cooperative planning and information skills that are embedded in the Policy have been well-accepted across the system. New documentation is needed to update learning outcomes and to provide direction for integrating new technologies and the concept of information literacy. Future directions for professional development for teacher-librarians, principals and classroom teachers were outlined and specific needs were identified in the areas of staffing, rebuilding school library collections and connecting technology initiatives and resource-based learning.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".