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Record W4306175753 · doi:10.1002/pra2.611

Sharing Research Design, Methods and Process Information in and out of Academia

2022· article· en· W4306175753 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2022
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsUniversity of British Columbia
FundersH2020 European Research CouncilHorizon 2020 Framework ProgrammeUniversity of British ColumbiaEuropean Commission
KeywordsTransparency (behavior)DisciplineProcess (computing)Data sharingInformation sharingKnowledge sharingComputer scienceKnowledge managementSociologyPublic relationsPsychologyPolitical scienceWorld Wide WebSocial science

Abstract

fetched live from OpenAlex

Abstract An interview study of (N=) 16 senior researchers at a major Canadian research university shows that researchers use a broad range of means to share information about research process, methods and design to different audiences. The purpose of sharing information on these aspects of research is to enable redoing and replicating earlier studies, to preserve knowledge of how studies were conducted, to understand data, and because of the social pressure to share. Time as a barrier and distance to overcome, disciplinary and contextual variation have a major impact on sharing. In the light of the findings, a one size fits all approach is unlikely to succeed. It is critical to choose appropriate methods that help to focus on what is relevant to share in particular disciplinary contexts, and for specific audiences and goals of transparency.

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)0.021
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.040
Open science0.0010.002
Research integrity0.0000.000
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 head0.122
GPT teacher head0.452
Teacher spread0.329 · 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