Censorship: What we’re <i>trying</i> to say…Drafting the IFLA Statement on Censorship
Bibliographic record
Abstract
On 25 August 2019, the Executive Committee of the International Federation of Library Associations and Institutions (IFLA) endorsed and published a new IFLA Statement on Censorship. The statement had been drafted by the IFLA Freedom of Access to Information and Freedom of Expression (FAIFE) Advisory Committee over the course of the previous year. The present article describes the process of creating the statement, highlighting several questions that the committee had to consider along the way. For example, in choosing a definition of censorship, the committee decided to create one that was conceptually limited and linguistically neutral (in the sense of not simply borrowing the existing definition of a national association). As well, the committee needed to explain why censorship was problematic, essentially proposing that it offends against the library principle of equity of access to information. The committee also had to consider how to account for the apparent acceptability in most societies of some forms of censorship, in spite of the generally problematic nature of censorship, and proposed that, as suggested by the United Nations’ Universal Declaration of Human Rights, Article 29, some limitations on liberties may be permissible for the general welfare of society – though not to the extent that the general concept of FAIFE is significantly compromised.
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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 | 0.028 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.031 |
| Scholarly communication | 0.029 | 0.025 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.014 | 0.024 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".