The Social Disruptiveness of Digital Agricultural Technologies: Asking Questions in the Context(s) that Matter
Bibliographic record
Abstract
Agriculture and food, the sector at the centre of many debates on technology driven human civilization, may be at the onset of another transformation: a transformation showing glimpse of both old and new revolutionary and incremental change in what farming means, where and how it is done and our relationship to the land, especially within rural settings. Today, food and agricultural systems are once again experiencing what can be described as another technological surge, a digital-driven potential transition. Emerging technologies including mobile support systems, precision agricultural tools, drone technologies, RFID and blockchain, sensors, satellite system, just to mention a few, are being employed across the food system, a system intrinsically and extrinsically connected to the what and the how of the countryside. There is no hiding that these recent development holds broader implications for both agriculture and farming, and rurality at large. However, at present, we are oblivious to the particularities of these implications. But we need to start the conversations about the implications for the rural to adequately prepare for what it has in stock for rural development and restructuring. What I seek to do in my research is to begin to ask some social questions on the digital surge in agriculture, with specific emphasis on how it will affect practices and performalities of rurality across rural landscapes. It is my intention to spur initial discussions with this preliminary presentation and engage audiences in exploring specific forms of the rural and farming that should be considered in this emerging field.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".