Bibliographic record
Abstract
Often, the ordinariness of familiar terms or concepts belies their complexity and hidden sides, necessitating closer scrutiny. “Big data” is one such phenomenon, upon which Bronson and Knezevic shine a critical spotlight. Showing how current data sources and data collection technologies differ from those of the past, the authors make the case that current big data are more than neutral numbers, but benefit productivist food regimes. They point to the need for research to document the consequences of big data to a broader group of food systems models. Another popular phenomenon is the “food charter”: dozens of such manifestos have materialized across Canada in the past decade, signifying positive, united visions for the food systems of cities and regions. Or is that just one side of the coin? Spoel and Derkatch analyse the food charter as a “genre”, examining their rhetoric and embedded ideologies. They suggest that charters perform not just by reflecting inherent values, but by aspiring to shape a food system in an uncontested way.
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 | 0.028 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.017 | 0.066 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".