“You Got to Have FAIFE”: The Role of Free Access to Information and Freedom of Information
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide a discussion of the activities carried out to promote intellectual freedom as a human right. Design/methodology/approach Focuses on the work of FAIFE – the Freedom of Access to Information and Freedom of Expression, an apolitical body that is a vehicle of the International Federation of Library Associations and Institutions (IFLA). Describes the historic role of FAIFE, its successes, challenges faced, discusses future endeavors and assesses its long‐term success. Findings Assesses FAIFE’s principles and objectives and finds its initiatives exemplified in the transparent and free access online users have to its publications. Uses examples from Cuba, Tunisia, China and the USA to present issues of intellectual freedom and the involvement of FAIFE. Finds that intellectual awareness is increased through research and collaboration. A major challenge remains in financing FAIFE’s activities; and seeking funding detracts from its other objectives. Raises doubts over FAIFE’s long‐term success rate in bringing about significant change. Originality/value Of interest in the long‐term to assess how successful FAIFE’s influence on human rights issues has been.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.055 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".