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Record W6893644092 · doi:10.5281/zenodo.3269474

DATA STEWARDSHIP ON THE MAP: A STUDY OF TASK AND ROLES IN DUTCH RESEARCH INSTITUTES

2019· article· en· W6893644092 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsImpact
Fundersnot available
KeywordsTask (project management)Stewardship (theology)Task forcePoint (geometry)Data collection

Abstract

fetched live from OpenAlex

A lightning talk about the report 'Data stewardship on the map: A study of tasks and roles in Dutch research institutes' recently delivered by the Datastewardship Task Force of the Dutch National Coordination Point Research Data Management. This short talk focuses on the outcomes most relevant for the digital humanities.

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)0.042
metaresearch head score (Gemma)0.109
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.109
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.012
Science and technology studies0.0060.008
Scholarly communication0.0130.013
Open science0.0020.011
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.196
GPT teacher head0.348
Teacher spread0.152 · 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 designQualitative
DomainMethods
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
Published2019
Admission routes1
Has abstractyes

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