MétaCan
Menu
Back to cohort
Record W1970555408 · doi:10.1087/095315108x254476

Data, disciplines, and scholarly publishing

2007· article· en· W1970555408 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLearned Publishing · 2007
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsBC Studies
Fundersnot available
KeywordsScholarly communicationPublishingPublicationScholarshipIncentiveValue (mathematics)Computer scienceDigital scholarshipData scienceProduct (mathematics)World Wide WebLibrary sciencePolitical science

Abstract

fetched live from OpenAlex

ABSTRACT Data are becoming an essential product of scholarship, complementing the roles of journal articles, papers, and books. Research data can be reused to ask new questions, to replicate studies, and to verify research findings. Data become even more valuable when linked to publications and other related resources to form a value chain. Types and uses of data vary widely between disciplines, as do the online availability of publications and the incentives of scholars to publish their data. Publishers, scholars, and librarians each have roles to play in constructing a new scholarly information infrastructure for e‐research. Technical, policy, and institutional components are maturing; the next steps are to integrate them into a coherent whole. Achieving a critical mass of datasets in public repositories, with links to and from publisher databases, is the most promising solution to maintaining and sustaining the scholarly record in digital form.

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.

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)0.028
metaresearch head score (Gemma)0.037
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Open science
Consensus categoriesScholarly communication, Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.3950.825
Open science0.0130.016
Research integrity0.0000.002
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 head0.201
GPT teacher head0.389
Teacher spread0.188 · 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