Vagabond: The Trans-Species Ecologies of Plant/Human Encounters
Bibliographic record
Abstract
[First paragraph] The opening scene of the acclaimed documentary King Corn (2007) shows Ian Cheney and Curtis Ellis, main protagonists, learning that corn constitutes one of the main carbon molecules of their hair. Segue to introduce the crop’s omnipresence in North American processed foods, principally used as sweetener, starch and animal feeds, the almost banal scientific fact presented in this scene is mesmerizing, providing a somewhat embodied support to the popular environmentalist saying “you are what you eat,” or to Donna Haraway’s poetic understanding of bodies and species as “full of their own others, full of messmates, of companions” (Haraway 2008, 165). Corn has indeed subtly made its way into our body, bite after bite, making it hard not to share Ian and Curtis’ awe while watching the film’s opening scene as it suggests that we, eaters of North American food, unknowingly became corn. Well established as the darling crop of nutritional technoscience, the introduction of genetically engineered corn in the late nineties juxtaposed to its wide presence in processed foods has spawned important political resistance, especially within Indigenous communities in Mexico. From street protest, field-testing to heirloom seeds international distribution, what is it exactly these activists were so desperately trying to protect?
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".