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Record W2010640343 · doi:10.1002/meet.1450440370

Scientific infrastructure design: Information environments and knowledge provinces

2007· article· en· W2010640343 on OpenAlexaff
Karen S. Baker, Florence Millerand

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2007
Typearticle
Languageen
FieldDecision Sciences
TopicPersonal Information Management and User Behavior
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsDisintermediationSociotechnical systemKnowledge managementInformation infrastructureConceptual frameworkComputer scienceInformation systemData scienceSociology of scientific knowledgeSociologyWorld Wide WebEngineeringSocial science

Abstract

fetched live from OpenAlex

Abstract Conceptual models and design processes shape the practice of information infrastructure building in the sciences. We consider two distinct perspectives: (i) a cyber view of disintermediation where information technology enables data flow from the ‘field’ and on to the digital doorstep of the general end‐user, and (ii) an intermediated view with bidirectional communications where local participants act as mediators within an information environment. Drawing from the literatures of information systems and science studies, we argue that differences in conceptual models have critical implications for users and their working environments. While the cyber view is receiving a lot of attention in current scientific efforts, highlighting the multiplicity of knowledge provinces with their respective worldviews opens up understandings of sociotechnical design processes and of knowledge work. The concept of a range of knowledge provinces enables description of dynamic configurations with shifting boundaries and supports planning for a diversity of arrangements across the digital landscape.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.004
Scholarly communication0.0010.010
Open science0.0010.001
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 head0.055
GPT teacher head0.351
Teacher spread0.296 · 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 teacher head, not a consensus.

Study designOther design
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

Citations13
Published2007
Admission routes1
Has abstractyes

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