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Record W2132923569 · doi:10.1002/meet.2009.1450460144

Disruptive technologies in health information landscapes: The case of diabetes and HbA1c

2009· article· en· W2132923569 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.

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCompetitive and Knowledge Intelligence
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSession (web analytics)ViewpointsMultidisciplinary approachBlogospherePanel discussionKnowledge managementComputer scienceData scienceSociologyBusinessWorld Wide WebAdvertisingSocial science

Abstract

fetched live from OpenAlex

Abstract This technical session, building on theories of disruptive technologies, offers a demonstration of strategic network analysis tools that are currently little discussed in the literatures relating to information analysis. Panel members will stimulate debate by addressing the topical subject of blogosphere analysis from contrasting and complementary viewpoints relating to competitive intelligence and marketing, information seeking and use, network analysis and the concept of disruptive technologies. The range of expertise represented by the multidisciplinary makeup of the panel will help ensure a richly informative and lively session. In addition, the session will provide a forum for discussion about the role of weblogs in the communication of specialized information to both lay and expert communities, as well as a discussion about approaches and techniques for blogosphere analysis in general.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.003
Open science0.0000.000
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 head0.009
GPT teacher head0.255
Teacher spread0.246 · 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