MétaCan
Menu
Back to cohort
Record W2163877954 · doi:10.5772/37533

Applying Social Sciences Research for Public Benefit Using Knowledge Mobilization and Social Media

2012· book-chapter· en· W2163877954 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

VenueInTech eBooks · 2012
Typebook-chapter
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsYork University
Fundersnot available
KeywordsSocial mobilizationMobilizationSocial mediaPolitical scienceResource mobilizationSociologyPublic relationsSocial movement

Abstract

fetched live from OpenAlex

The social sciences and humanities (SSH) matter. They matter because they help us understand and address wicked problems. Wicked problems are persistent problems about which there is little agreement on solutions. Not all the stakeholders are known, end points are equivocal and when interventions are introduced the problems themselves might change. We can address wicked problems but we have a tough time eradicating them. Wicked problems “occur in a social context; the greater the disagreement among stakeholders, the more wicked the problem. In fact, it’s the social complexity of wicked problems as much as their technical difficulties that make them tough to manage” (Camillus 2008, 100). Wicked problems are therefore social problems. Wicked problems are problems of the social sciences.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.827
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.002
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.416
GPT teacher head0.435
Teacher spread0.019 · 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