MétaCan
Menu
Back to cohort
Record W2752343506 · doi:10.15171/ijhpm.2017.103

How Political Science Can Contribute to Public Health: A Response to Gagnon and Colleagues

2017· letter· en· W2752343506 on OpenAlexaff
Anita Kothari, Ruta Valaitis, Vera Etches, Marc Lefebvre, Cal Martell, S. McElhone, Ruth Sanderson, Louise Simmons

Bibliographic record

VenueInternational Journal of Health Policy and Management · 2017
Typeletter
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsRegional Municipality of NiagaraChamplain Regional CollegeOttawa Public HealthMcMaster UniversityWestern University
Fundersnot available
KeywordsPoliticsPolitical sciencePublic healthPublic relationsPublic administrationPsychologyMedicineNursingLaw

Abstract

fetched live from OpenAlex

As public health scholars

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)0.031
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.087
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.099
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0140.021
Scholarly communication0.0140.023
Open science0.0060.010
Research integrity0.0870.156
Insufficient payload (model declined to judge)0.0060.004

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 head0.097
GPT teacher head0.444
Teacher spread0.348 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Health Policy and ManagementSame topicNursing Education, Practice, and LeadershipFrench-language works237,207