Bibliographic record
Abstract
Abstract A methodology for plant qualitative research is at an early stage of development. While conducting a multispecies ethnography of gardeners and the plants they grow for food in a neighborhood in transition from social housing to a mixed-income community in Toronto, the author wondered, How to account for plants and their agency? What is evidence of vegetal politics? What is a multispecies ethnographer doing when decentering the human in relation to garden plants, beyond what is un-done ontologically? This article situates itself in the plant turn and proposes a methodology to account for plant agency in gardens and to identify vegetal politics. The author builds on the methodological work of other scholars of human-plant relations and posthumanist notions of relational agency to develop a three-step method: (1) recognize plant time, (2) participate with plants, and (3) scale up. Central to the methodology—and a key contribution the author puts forward—is a shift away from the researcher considering plants as individuals and instead understanding plant communities as the unit of analysis. This shift in scale, while recognizing plant time and the relational agency of plants, permits the identification of vegetal politics and has allowed the author to theorize plants as political actors in cities that support health.
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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 | 0.028 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.316 | 0.081 |
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".