Bibliographic record
Abstract
This paper takes what has been termed the “epidemic of obesity” as the point of departure to examine the way in which political economic factors intersect with diet and nutrition to determine adverse health outcomes. The paper proposes several concepts to better understand the dynamics of the “foodscape” – institutional sites for the merchandising and consumption of food. These include the concepts of “spatial colonization” and “pseudo foods.” With a focus on critical dimensions of the contemporary “foodscape,” principally supermarket merchandising practices, as well as trends in other food vending operations, the paper explores incentives that motivate capital to “spatially colonize” the foodscape with aggressively promoted high fat/high sugar “pseudo foods.” The paper reports on extensive research on trade industry publications as well as data collected through onsite investigations of supermarket practices of the three largest Canadian retail supermarket operations. In addition, current merchandising practices of convenience chain store operations and some non-traditional food vending sites are examined. In concluding, it is argued that the rapidly evolving interdisciplinary debate around the obesity crisis would benefit considerably from the insights to be gained from political economic analysis of retail food industry practices and trends.
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 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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".