Review of First we eat: Food sovereignty north of 60
Bibliographic record
Abstract
Suzanne Crocker’s 2020 film First we eat documents her and her family’s efforts to spend an entire year eating only food that can be grown, gathered, and hunted around Dawson City, Yukon, in the traditional territory of the Tr’ondëk Hwëch’in. Living 300 km south of the Arctic Circle, Crocker’s experiment was spurred by a landslide that disrupted the supply of imported foods into the territory, serving as a wake-up call to food system vulnerability, including increasing and unpredictable climate impacts. First we eat offers an educational glimpse into local northern food systems and food sovereignty that generates reflection on the importance of connecting and learning through communal food networks. In addition to community food connections, the food literacy, self-sufficiency, creativity, change, and challenge involved in eating locally for one year reflect the disconnect between consumers, producers, and land in contemporary food systems. The Crocker family learns a great deal about food harvesting, production, processing, and storage, much of which reflects the knowledge embedded in local and Indigenous food practices. First we eat serves as a starting point for discussions about food security, localizing food systems, and food sovereignty, as well as critical reflection on how Indigenous land, food sovereignty, and knowledge are central to these discussions. Crocker’s ambition to eat entirely locally for one year inspires reconnection with food, land, and community, encouraging viewers to explore how they might become more engaged with their local food system.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".