Bibliographic record
Abstract
Food and Agriculture are two of the most direct factors in human and environment health. However, the global industrial food system benefits large agribusinesses, and skews the state – industry power dynamic in the favour of economic growth, not human or environmental wellbeing. Traditionally, agribusiness exercises power in three key ways – media and outreach, market power, and lobbying – impacting agricultural, food and nutrition policy. Therefore, in cases where federal policy changes, it can generally be understood as a response to a shift in one or more of these three factors. In early 2019 Health Canada released Canada’s Food Guide, the newest edition in over 70 years of nutrition advising. However, unlike prior versions which prioritized industry over nutrition, this new food guide is a more accurate reflection of both nutrition and environmental research. Most remarkable in this change, is that the power and interest of agribusiness in Canada does not appear to have changed considerably in order to initiate these changes. As such, five additional factors that collectively minimized the power given to agribusiness are explored - increased awareness of nutritional information, the rise of vegans and vegetarians, demographic and political economy trends, social pressure and bureaucratic changes, and consideration of diet co-benefits and costs. I conclude by highlighting that regardless of the reasons behind the changes to Canada’s Food Guide, without changes to agriculture policy to meaningfully increase the accessibility of the recommended food, the new recommendations are unlikely to impact Canadian eating habits.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".