Sociotechnical Imaginaries: A Possible Contribution to Science Education
Bibliographic record
Abstract
This is a conceptual paper that highlights notions of sociotechnical imaginaries (STIs; Jasanoff, 2015) from fields of Science and Technology Studies (STS) that seem relevant to science education aimed at preparing critical and active citizens (Bencze, 2017). We extend our discussion to fields of future studies in science education to argue that a needed direction is not merely to get students to imagine desired (often personalized) futures (especially given social and environmental harms), but to interrogate how products of science and technology seem to delimit kinds of futures we ought to desire. That is, technoscientific futures are not just out there, but are already present, actively fashioning current practices and values. Drawing from STS literature, we demonstrate how STIs are enacted through two current technoscientific products: self-tracking devices and algorithms. We argue that such technoscience products have an active role in constructing certain kinds of individuals/publics (e.g., quantified citizens, calculated publics). Roles of material technologies in normalizing moral and political visions and future orientations need to be explicitly addressed in re-centering nature of technology as inseparable from nature of science (Roth, 2001). Notions of STIs further offer more nuanced approaches to discuss power at the interface of the public/private within STS Education (Pedretti & Nazir, 2011). Finally, notions of STIs may present us with new ways for (re-)encountering affect in science education (Alsop, 2016), as feelings of hope and anxieties contour (how we come to re-envision) imaginaries grounded in technoscientific worlds.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.057 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".