Provocations from the ‘STS as a Critical Pedagogy’ Workshop
Bibliographic record
Abstract
This research article is a collaborative set of reflections and provocations stemming from the National Science Foundation (NSF) funded workshop on STS as a Critical Pedagogy, hosted online during the summer of 2021 by Shannon N. Conley and Emily York at James Madison University. The workshop occurred over four separate sessions, bringing together forty participants (including six undergraduate students who contributed as both facilitators and research assistants). Participants self-organized into panels, leading the workshop collective to engage a host of questions, challenges, methods, and practices related to STS and critical pedagogy. Questions included the following. What characterizes critical STS pedagogies? How are critical STS pedagogies enabled and constrained by our institutional and disciplinary locations? What makes STS pedagogies travel? How might we imagine STS pedagogies otherwise? How do our pedagogies shape our research and engagement in the world? How might we critically interrogate the boundaries between research, teaching, service, and engagement, and what becomes visible when we do so?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.029 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".