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Record W4288686143 · doi:10.4242/balisagevol27.david01

BITS for Government Information?

2022· article· en· W4288686143 on OpenAlexaffabout
Ravit H. David

Bibliographic record

VenueBalisage series on markup technologies · 2022
Typearticle
Languageen
FieldComputer Science
TopicMathematics, Computing, and Information Processing
Canadian institutionsOntario Council of University LibrariesUniversity of Toronto
Fundersnot available
KeywordsMetadataSuiteComputer scienceWorld Wide WebSchema (genetic algorithms)Government (linguistics)RewritingVocabularyInformation retrievalPolitical science

Abstract

fetched live from OpenAlex

Scholars Portal, a service of the Ontario Council of University Libraries, provides multiple levels of access and preservation to scholarly packages of Ebooks. We have created an in-house custom modification of the Book Interchange Tag Suite (BITS, a sister vocabulary to the Journal Article Tag Suite, JATS) to describe EBooks. A recent strategic decision to host government information has posed several challenges with our BITS modification and required a new in-house schema to accommodate specific metadata requirements posed by the somehow different nature of govinfo content. We’ll look at some of these challenges, then examine ways BITS can accommodate metadata that isn’t necessarily standard Ebook metadata.

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)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.198
Threshold uncertainty score0.664

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0030.005
Scholarly communication0.0150.024
Open science0.0010.007
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.1980.117

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 head0.009
GPT teacher head0.207
Teacher spread0.199 · 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.

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".

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Citations1
Published2022
Admission routes2
Has abstractyes

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