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Record W2044093619 · doi:10.1002/meet.14504201237

Interoperability strategies for scientific cyberinfrastructure: Research and practice

2005· article· en· W2044093619 on OpenAlexfundno aff
Karen S. Baker, David Ribes, Florence Millerand, Geoffrey C. Bowker

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2005
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsnot available
FundersDivision of Social and Economic SciencesSocial Sciences and Humanities Research Council of CanadaUniversity of California, San DiegoNational Science Foundation
KeywordsCyberinfrastructureInteroperabilityKnowledge managementData scienceRubricComputer scienceScale (ratio)World Wide WebSociology

Abstract

fetched live from OpenAlex

Abstract The development of new infrastructures for research and collaboration are occurring together with changes in expectations for scientific knowledge. New vocabularies and perspectives are developing with social and organizational practices of science changing concurrently but at different rates. Between the new infrastructures and the new perspectives, we are changing both how we know and what it is to know. A recently initiated three year project supported by the NSF Human Social Dynamics Program (Interoperability Strategies for Scientific Cyberinfrastructure: A Comparative Study) brings together work with three established research collaborations on large‐scale information infrastructures in order to understand through comparative study particular configurations of technologies, communities, and organizations. Despite specific alignments of technical commitment, community involvement and organizational structure, all the projects fall under a common rubric of achieving for data interoperability.

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)0.083
metaresearch head score (Gemma)0.067
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score0.437

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0830.067
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.007
Science and technology studies0.0050.038
Scholarly communication0.0270.034
Open science0.0060.012
Research integrity0.0100.006
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 head0.110
GPT teacher head0.427
Teacher spread0.317 · 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
Domainnot available
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

Citations16
Published2005
Admission routes1
Has abstractyes

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