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Record W4319662495 · doi:10.7191/jeslib.670

Special Issue: 2022 Research Data Access and Preservation (RDAP) Summit

2023· article· en· W4319662495 on OpenAlexaff
Caitlin Bakker, Kathryn Brooks, Jeanine Finn, Paria Aria

Bibliographic record

VenueJournal of eScience Librarianship · 2023
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsSummitTheme (computing)Research dataComputer sciencePolitical scienceWorld Wide WebData scienceGeographyCartography

Abstract

fetched live from OpenAlex

2022 marked the Research Data Access and Preservation (RDAP) Summit’s second fully virtual conference, which focused on the theme of Envisioning an Inclusive Data Future. Presenters shared perspectives on new and emerging services, as well as observations of existing and prior practices, and strategies for re-envisioning these activities. The Summit built upon last year’s theme of Radical Change and Data, which encouraged presenters and attendees to consider the intersections between data and social change.

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)0.010
metaresearch head score (Gemma)0.019
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 score0.983
Threshold uncertainty score0.723

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.002
Science and technology studies0.0060.002
Scholarly communication0.0170.008
Open science0.0040.006
Research integrity0.0100.009
Insufficient payload (model declined to judge)0.2160.109

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 head0.580
GPT teacher head0.493
Teacher spread0.087 · 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
Published2023
Admission routes1
Has abstractyes

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