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Record W6931261137 · doi:10.5281/zenodo.6902822

"Good things come in small packets": Investigating Humanities Research Data Practices: A Community of Practice Approach

2022· article· en· W6931261137 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsDigital humanitiesData collectionField (mathematics)Context (archaeology)Key (lock)

Abstract

fetched live from OpenAlex

A brief description of the Humanities Data Inquiry project and the problem of Humanities Data it attempts to address. Includes a video and slide deck.

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)0.438
metaresearch head score (Gemma)0.486
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesMetaresearch
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.693

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4380.486
Meta-epidemiology (narrow)0.0010.003
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0190.020
Science and technology studies0.0350.083
Scholarly communication0.0350.041
Open science0.0100.052
Research integrity0.0160.016
Insufficient payload (model declined to judge)0.0050.001

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 head0.369
GPT teacher head0.395
Teacher spread0.026 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designQualitative
DomainReproducibility
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicPancreatic and Hepatic Oncology Research→French-language works237,207→