THE 2007 INTERNATIONAL LIBRARY SURVEY IN LATINAMERICA AND THE CARIBBEAN
Bibliographic record
Abstract
This paper summarises the results of the 2007 UNESCO Institute for Statistics/ IFLA/ISO library survey which was conducted across Latin America and the Caribbean.UIS is a founder member of the Partnership for the Measurement of ICTs for Development the official international body responsible for the statistical aspects of the follow up to the World Summit on the Information Society.The priority for the culture team at the Institute is the revision of the 1986 UNESCO framework for cultural statistics, while in communications the team is working on international statistics for the use of ICTs in Education, and information literacy.The 2007 library survey has collected statistics on libraries in each of the Latin American subregions; Central America, Caribbean, South America.The questionnaire covered both public libraries and higher education libraries but most responses concerned public libraries only.A mixed response was obtained to 'new topics' such as; internet connections, e-books, and database access.The papers discusses the lessons learnt from the survey, including where response rates or definitions might be improved as well as areas where there is simply a lack of data.The potential for an international survey of library statistics will be revisited, as well as consideration of the minimum statistical reporting requirement for a functioning national library system.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.030 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".