Ladange, Adange, Jeetange: The Farmers' Movement and Its Virtual Spaces
Bibliographic record
Abstract
The farmers' movement began in November 2020, when more than 300,000 protesters marched towards New Delhi in India in opposition to new agrarian acts introduced by the Indian central government. Organised by over 400 farmers' unions along with other organisations, for the next year, farmers and allied protesters set up bases around New Delhi and sustained the movement leading to its eventual success. We conducted 20 semi-structured interviews with participants from the movement and explored the social organisation, the underlying technical infrastructures, and how collective action was organised. We outline how the social media ecosystem enabled hybrid forms of organisational structures and facilitated coalition-building between diverse groups. Further, the movement created and disseminated alternative media that opposed mainstream media narratives and facilitated community-building. We discuss how designed technologies and spaces can support social movements in the face of powerful antagonistic actors.
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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 | 0.001 | 0.000 |
| 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 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".