We become gardens: intersectional methodologies for mutual flourishing
Bibliographic record
Abstract
In this paper, members of the Re-creation Collective offer key methodological practices that nourish us and our work together. These include deep visiting, intersectional praxis, (re)visiting accessibility, and accountability revisited. This methodological sharing is not intended to be prescriptive: there is not one ‘recipe’ for doing this work. We offer an emergent collection of hows, rather than a predictable list of whats. Nor is this sharing intended to be descriptive of all of the methodological choices we have made. Rather, we intend this sharing as inscriptive: some of the most profound ways our processes have marked us; a way to leave traces of our learnings for others; an offering of approaches for carving out methodological spaces that are capacious and profound enough to bring our many selves into.
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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 | 0.051 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.012 | 0.070 |
| Scholarly communication | 0.023 | 0.034 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".