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Record W3128883637 · doi:10.1177/0955749020954133

Censorship: What we’re <i>trying</i> to say…Drafting the IFLA Statement on Censorship

2020· article· en· W3128883637 on OpenAlexaff
Brent Roe

Bibliographic record

VenueAlexandria The Journal of National and International Library and Information Issues · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsLaurentian University
Fundersnot available
KeywordsCensorshipStatement (logic)Political scienceDeclarationLawCivil libertiesSociologyPolitics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.875
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.014
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.305
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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