Agriculture by Algorithm: Big Data, Digitalization, and Biotechnology Under Climate Change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Based on textual analysis of publicly available documents published by the Food and Agriculture Organization, Bayer, and its partner delivery start-ups, this paper provides insight into the data-driven processes and technologies that are transforming agriculture into digitally standardized precision farming. Digitalization and biotechnology are intertwined within an “agriculture by algorithm” directed toward eliminating site-specific variations on the farm and optimizing efficiency for increasing yield. This new agriculture, through measurable indicators, calculative metrics, and algorithmic modeling, relies on a commensuration process that converts agroecologically and experientially diverse ways of knowing into standard data units within Big Data. Supported by multistakeholder platforms, blended cofinancing, and venture capital, “agriculture by algorithm” is expanding the epistemic dominance of quantification into village farming, rendering local farming knowledges and assessments invisible and/or irrelevant.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it