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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 OpenAlexafffundabout
Isto Huvila, Luanne Sinnamon

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.

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)0.280
metaresearch head score (Gemma)0.259
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: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.888

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2800.259
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.008
Science and technology studies0.0160.022
Scholarly communication0.0210.012
Open science0.0050.014
Research integrity0.0030.003
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 head0.122
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

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

Citations13
Published2022
Admission routes3
Has abstractyes

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