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Record W2794723841

Fat : an exploration into the political ramifications of excess adipose tissue in Canada

2011· dissertation· en· W2794723841 on OpenAlexaboutno aff
Chad Vernon Douglas Stewart

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsnot available
Fundersnot available
KeywordsAdipose tissuePoliticsPolitical scienceMedicineEnvironmental ethicsInternal medicineLawPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

The state and individual must both understand that the increase in fat rates is a social phenomenon that requires reconciliation between collective and individual participation. A social movement needs to be generated that seeks solutions to this health phenomenon through preventative health measures; because the state’s current reactionary response does not address the factors that contribute to increased fat. These factors transcend the direct relationship between an individual, food and exercise, and also involve power. The current policy definition of fat is incorrect because it does not address the multiple variables that have generated an increase in common indicators of obesity; rather, it relies on inaccurate measurement systems, differing conceptions of the healthy individual, and narrow understandings of what causes obesity. The result is the current paralysis of policy reform. This thesis provides solutions that reconcile the current political definition with my own in order to advocate health promotion strategies that activate both the citizen and the state.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.142
Threshold uncertainty score0.995

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0150.005
Scholarly communication0.0060.001
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.000

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 head0.135
GPT teacher head0.478
Teacher spread0.344 · 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 designQualitative
Domainnot available
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
Published2011
Admission routes1
Has abstractno

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