MétaCan
Menu
Back to cohort
Record W2541009371

Finding the right frame: What is the problem represented to be in a national tuberculosis strategy

2015· article· en· W2541009371 on OpenAlexaffabout
Shayna Campbell

Bibliographic record

VenueGlobal Health: Annual Review · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFraming (construction)TuberculosisPopulationPublic healthPolitical scienceAgency (philosophy)Social issuesEconomic growthMedicineSociologyEnvironmental healthLawGeographyEconomicsSocial science
DOInot available

Abstract

fetched live from OpenAlex

This paper examines how a problem representation that targets specific populations impacted the formulation of a national tuberculosis strategy in Canada. Globally, one third of the world population is infected with tuberculosis (1). Tuberculosis has garnered increased international attention with the emergence of multi-drug resistant TB. International standards endorse that low incidence countries enact TB policies that address the most vulnerable and hard to reach groups (2). In Canada, a low incidence country, two groups are disproportionately affected by TB: the Aboriginal and foreign-born populations (3). In 2014, Health Canada and the Public Health Agency of Canada released a national strategy for TB (3) . This analysis of the “Framework for Action” policy document will focus on problem framing using Bacchi’s theory of “What’s the problem represented to be?” (4) The choice of a problem frame reduces the complexity of tuberculosis and its political and social connections. Specifically, problem frames containing target populations are influenced by the social constructs of that population. This paper will argue that narrowing the frame of tuberculosis to a development issue in the Aboriginal population and a security issue for the foreign-born population has implications at the domestic and international level.

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)0.018
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.298
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.291
GPT teacher head0.570
Teacher spread0.280 · 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 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

Citations0
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueGlobal Health: Annual ReviewSame topicEvaluation and Performance AssessmentFrench-language works237,207