Patchworking Response-ability in Science and Technology Education
Bibliographic record
Abstract
Within science and technology education, concepts of justice, in/equity, and ethics within science education are simultaneously ubiquitous, necessary, yet un(der)theorized. Consequently, the potential for reproducing and reifying systems of power remains ever present. In response, there is a recent but growing movement within science and technology education that follows the call by Kayumova and colleagues (2019) to move “from empowerment to response-ability.” It is a call to collectively organize, reconfigure, and reimagine science and technology education by taking seriously critiques of Western modern science and technology from its co-constitutive exteriority (e.g., feminist critiques). Herein, we pursue the (re)opening of responsiveness with/in methodology by juxtaposing differential, partial, and situated accounts of response-ability: de/colonizing the Anthropocene in science teacher education in Canada (Higgins); speculative fiction at the science-ethics nexus in secondary schooling in Australia (Mahy); and a reciprocal model for teaching and learning computational competencies with Latinx youth in the US (Aghasaleh and Enderle).
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.103 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".