Disparities in the availability of fruit, vegetables and snack foods by neighborhood socioeconomic status in supermarkets and grocery stores in Montréal, Canada
Bibliographic record
Abstract
First, I would like to highlight the support of my supervisors, Chalida Svastisalee and Pernille Due, who have been instrumental in moving this project forward.To Chalida, thank you for your availability and instant feedback in the wording and the whole content of this dissertation.To Pernille, thank you for providing me with an overall view of the project as well as advice on technicalities.Furthermore, the achievement of this project would have been otherwise without the help of my boyfriend Guillaume, who put up with my countless talks about my thesis on top of sacrificing days off to help me with the measurements in supermarkets.Thank you for your dedication and your patience.To my parents as well, thank you for having constantly believed in me and backed me up in my projects.To my friends and colleagues, thank you for brainstorming with me even though this project sounded very different than yours
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".