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Record W2990365614 · doi:10.1289/isee.2016.4844

Gender-based analysis of environmental factors

2016· article· en· W2990365614 on OpenAlex
Donna Mergler

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

VenueISEE Conference Abstracts · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsBiological sexPsychologyEnvironmental healthSex characteristicsIntervention (counseling)Environmental epidemiologyDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

We all know that boys and girls, men and women differ biologically and in their social and power relations throughout the life span. However, research in environmental epidemiology often does not consider sex and/or gender as a characteristic that requires in-depth consideration. The terms sex (biological attributes) and gender (socially constructed roles and behaviour) are often confused and used interchangeably. Throughout the lifespan, sex and gender are in interaction and both may play a role in influencing exposure and effect. Adjusting for sex/gender as a covariate does not necessarily take into account the underlying biological processes, the gender-related environmental profile of exposure, the social modifiers or the consequences on health and well-being. Integration of sex and gender considerations into research design and analyses would improve our understanding of exposure pathways and the associations between environment and health outcomes, as well as providing better gender- and sex-sensitive intervention strategies to reduce harmful environmental exposures and improve health.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.000

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 head0.085
GPT teacher head0.300
Teacher spread0.215 · 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