LIMITS OF THE ‘GLOBAL STATISTICS’ MODEL AND SOME EXAMPLES OF HOW RESULTS ARE USED
Bibliographic record
Abstract
This presentation will begin with a short history of UNESCO's survey of libraries up to the implementation of the Pilot Survey on Library Statistics in the Latin American context.The 'Global Statistics' model will then be briefly situated within a literature review of the assessment of libraries, focusing on the reasons behind the assessment and the criteria used to draw up indicators.In the course of presenting indicators that have been retained, a quick diversion will be made into literacy indicators at the library level.Some of the project's results will be unveiled in relation to the link between digital information and libraries, as well as literacy rates.The overlapping of indicators around loans data will also be examined.The final portion of the presentation will report on the impact of the project on the ISO 2789 standard and examine the limits of the 'Global Statistics' model.The presentation will close with a look at the evolution of a statistical culture within libraries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".