“D'un Îlot de Chaleur à un Îlot de Fraîcheur”1 \nA Co-op Intervenes Upon Itself
Bibliographic record
Abstract
In the spring of 2019, I was referred to Touski, a co-op in Montreal, who had been experiencing debilitating conflict in their organization, and was unable to get to the bottom of it. Our work together became my AHSC 698 Senior Project. What follows is a series of reflections on my experiences with the client system, inspired and informed by theories from the field. Another consultant might have responded differently to the issues faced by this organization. While I drew on well-proven practices I knew, and others I came to know for the first time, I now believe that what I was doing was working intuitively with the group, and their challenges (Lipson Lawrence, 2012). I listened deeply, and unremittingly, throughout the process, with all my senses (Holman, 2010), and I exercised a kind of vigilance to the quality of presence I brought to each of my interactions with the client. I did not realize it at the time, but what I was practicing, and heavily relying upon, was my self as instrument.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.014 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.083 | 0.026 |
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".