Collecting Data About the Impact of School Libraries on Education, at International Level, in Developing, Emerging and Developed Countries
Bibliographic record
Abstract
This paper describes “work in progress”. It outlines attempts being made by the IASL Research SIG, the ENSIL Foundation and the Royal Tropical Institute in Amsterdam to collect consistent data about school libraries in developing, emerging and developed countries, using an international definition of what a school library actually is. During a meeting of the IASL Research SIG on 24 January 2012 it was agreed that a set of simple questions (approx. 10 questions for each group) which could be answered by pupils, teachers, school librarians and school leaders in different countries throughout the world should be developed . Sets of questions are now being reviewed by a selected group of school library practitioners and academics and by a small sub-committee of the Research SIG. Using the agreed sets of questions, preliminary data will then be collected by a number (school) library associations or other affiliated organizations in different parts of the world. Initial progress and results will be presented. It is to be hoped that some (initial) useful data and comparisons will demonstrate the international scope and impact of school libraries to all stakeholders, at international level.
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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 | 0.025 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".