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Record W2938471675 · doi:10.5683/sp/e6lsvq

Research Data Management Survey of Queen's University, 2018

2018· dataset· en· W2938471675 on OpenAlexaff
Francine Berish, Alexandra Cooper, Jackie Druery, Jeremy Heil, Jeff Moon, Suzanne Maranda, Sharon Murphy, Nasser Saleh, Tatiana Zaraiskaya

Bibliographic record

VenueQSpace (Queen's University Library) · 2018
Typedataset
Languageen
FieldDecision Sciences
TopicData Quality and Management
Canadian institutionsOntario Council of University LibrariesQueen's University
Fundersnot available
KeywordsQueen (butterfly)RDMLibrary scienceData management planSurvey researchData managementResearch dataSociologyEngineeringManagementComputer scienceDatabasePedagogy

Abstract

fetched live from OpenAlex

Between 2015 and 2017, three Research Data Management (RDM) surveys were conducted by Queen’s University Library in collaboration with University Research Services. Each survey focused on a different set of disciplines: Engineering and Science (November 24-December 31, 2015); Social Science, Humanities, Business, Education, Law, and Policy Studies (SSHBELPS) (June-July 18, 2016); and Health Sciences (January 26-February 21, 2017).

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 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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
Consensus categoriesOpen science, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.230
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.006
Science and technology studies0.0010.002
Scholarly communication0.0010.005
Open science0.0210.037
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.173
GPT teacher head0.364
Teacher spread0.191 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2018
Admission routes1
Has abstractyes

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