‘Finprint’ technopolitics and the corporatisation of global food governance
Bibliographic record
Abstract
Abstract Our concern in this paper is the environmental ‘footprinting’ of food and its role as a source of technopolitical power in global food governance. Our case is the highly industrialised farmed salmon sector which currently generates metrics and carefully curated visualisations to promote this fish as a more sustainable and ‘climate friendly’ protein relative to animal protein produced on land. We show how these metrics and visualisations depend on an industrial production and measurement infrastructure. Significantly, this infrastructure and the metrics that it generates is being promoted as a ‘climate smart’ solution to small‐scale and extensive aquaculture in the Global South. Salmon aquaculture industry proposals for the transfer of technology from salmon farming to global aquaculture are explicitly articulated in global food governance and other institutional spaces. While there may be frictions in the transfer of salmon aquaculture's infrastructure of measurement to aquaculture in the Global South, our analysis suggests that environmental footprinting of food—and its associated measurement infrastructure—may be an emerging source of technopolitical power in increasingly corporatised global food governance systems.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.049 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".