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Impact‐Driven work: Tearing down the firewalls between research and practice

2015· article· en· W2336514543 on OpenAlexaff
Denise E. Agosto, Denise Bedford, Michael Khoo, Delia Neuman

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsYork University
Fundersnot available
KeywordsWork (physics)Public relationsTearingSociologyPolitical scienceKnowledge managementComputer scienceEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

ABSTRACT This panel will showcase information research that aims to have strong impact on community at the local or national level and offer ideas for increasing the impact of information research on communities. It will also engage audience members in an interactive discussion of ideas for increasing the real‐life impact of information research.

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)0.502
metaresearch head score (Gemma)0.499
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.498
Threshold uncertainty score0.615

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5020.499
Meta-epidemiology (narrow)0.0030.005
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0130.007
Science and technology studies0.0360.126
Scholarly communication0.0820.105
Open science0.0110.097
Research integrity0.0250.034
Insufficient payload (model declined to judge)0.0220.007

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 head0.088
GPT teacher head0.426
Teacher spread0.338 · 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 designTheoretical or conceptual
DomainMethods
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

Citations0
Published2015
Admission routes1
Has abstractyes

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