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Record W7097335728

Legal Deposit of on-line materials and National Bibliographies

2001· article· en· W7097335728 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicArtificial Intelligence Applications
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationCornerstoneOrder (exchange)DocumentationGeneral assemblyNational security
DOInot available

Abstract

fetched live from OpenAlex

Listening to our speakers today there can be no doubt about the role and value of national bibliographies. The challenge is to convince funding agencies that a national bibliography is a fundamental tool that if adequately supported can really contribute to the cultural development and economic growth of a country. A basic building block to achieving a truly comprehensive national bibliography is to ensure that all the publications of a country are included. An important tool to help achieve comprehensiveness is legal deposit. In the preface to the recently published revised edition of the Guidelines for Legal Deposit Legislation, I said: " the role of national libraries in ensuring universal and equitable access to information continues to be a cornerstone in the development of a knowledge society. A national library faces many challenges in ensuring that the published heritage of its country is acquired and preserved for all to use. An important vehicle in assisting national libraries meet this responsibility is legal deposit. " Most countries do rely on a legal instrument of some sort in order to ensure the comprehensiveness of their national deposit collection. In 1981, UNESCO published a study prepared by Dr. Jean Lunn from Canada entitled Guidelines for Legal Deposit Legislation. Interestingly enough this study resulted from a recommendation of the 1977 International Congress on National Bibliographies held in Paris. The idea was to develop model legislation which would assist countries in preparing their specific legislation and which would, and I quote, "serve as a basis for Member States in attaining national bibliographic control. " This work served the community well for a number of years, but as Dr Lunn’s guidelines primarily studied the issues in relation to print material it became increasingly evident that the Guidelines needed to be revised. In spite of regular discussions about this need at

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.036
metaresearch head score (Gemma)0.177
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.509

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.177
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0160.016
Science and technology studies0.0070.008
Scholarly communication0.0230.023
Open science0.0040.013
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.1520.122

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 head0.054
GPT teacher head0.319
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), not a consensus.

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
Published2001
Admission routes1
Has abstractyes

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