Ecological livelihoods of farmers and pollinators in the Himalayas: Doing critical physical geography using citizen science
Bibliographic record
Abstract
In farming communities dependent on the cultivation of pollinator‐dependent crops, the livelihoods of farmers are inextricably linked with pollinator health. A global pollination crisis interlinked with a crisis of food production and farmer livelihoods, exacerbated by processes of socio‐environmental change, is emblematic of the Anthropocene and of the kinds of ecosocial problems with which critical physical geography (CPG) engages. We propose examining the farmer‐pollinator system in the Indian Himalayas through an ecological livelihoods approach using a range of collaborative citizen science methods including bloom observations to document pollinator visits, plant phenological observations to document year‐round floral resource availability, and farm diaries to document orchard management practices. An ecological livelihoods approach draws on posthumanist theory, which has remained largely disengaged with methodological questions that are of concern to CPG. Citizen science, although widely used across a range of disciplines, has seen limited engagement in CPG. After elaborating some of the opportunities and challenges that an engagement between CPG, posthumanist theory, and citizen science opens up, we propose a methodology that would be simultaneously epistemological (understanding interdependence between livelihoods of farmers and pollinators) and ontological (imagining and building worlds where farmer and pollinator habitats are recomposed).
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".