Un/thinking with Thread/s: Needling Through Boundaries Related to COVID-19 and Medical Training
Bibliographic record
Abstract
This article draws on my connection with sewing threads, and explores how the 2020 Massive Microscopic Sensemaking (MMS) online challenge contributed to an emergent entanglement of timespacemattering related to COVID-19, teaching and researching medical learning in obstetrics, and thinking further with my PhD. It explores affirmative processes enacted during times of anxiety, when my thoughts needled through in-between spaces with different times and materials that were generative and productive. I explain my rhizomatic movements that bleed through conventional separations and boundary-making assumptions. I draw on Karen Barad’s agential realism to theorize the emergence of creative relationalities with artful artifacts enacted with medical undergraduate students, with participants in the MMS project, and with my own PhD during times of tension.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.040 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".