Bibliographic record
Abstract
How ten making & doing projects expand STS scholarship through a focus on knowledge expression and knowledge travel in addition to knowledge production. Making & doing projects expand STS scholarship to include the trajectories of STS knowledge flow beyond the boundaries of the field by actively interweaving knowledge expression and travel with knowledge production. In this edited volume, contributors from around the world present and critically assess ten empirical making & doing projects. They recount how their projects advance STS, and describe how they themselves learn from their interlocutors and the settings in which they do and share their STS work. A coda explains how the infrastructures of STS scholarship are broadening to include practices of making & doing. The contributors examine and reflect upon their dilemmas, frustrations, and failures, especially when these generate new practices that might not have occurred had their work not taken the form of making and doing scholarship. While each project raises a distinct set of scholarly issues, all of the projects include practices that express STS knowledge through “STS sensibilities” and attach those sensibilities to practices in empirical fields. The projects include one each in Argentina, Taiwan, Canada, and Denmark; two in the US; one in Austria, the UK, and multiple countries in Africa and Asia; one in the US and Latin America; one in the Netherlands and Australia; and one in an international network that includes members from Europe, the Americas, and Australia. Contributors Gary Lee Downey and Teun Zuiderent-Jerak; Yi-Ping Lin and Hsin-Hsing Chen; Dawn Nafus, Michael Guggenheim, Judith Kröll, and Bernd Kräftner; Hernán Thomas, Lucas Becerra, and Paula Juárez; Torben Elgaard Jensen, Andreas Birkbak, Anders Koed Madsen, and Anders Kristian Munk; Max Liboiron, Emily Simmonds, Edward Allen, Emily Wells, Jess Melvin, Alex Zahara, and Charles Mather; Jessica Mesman and Katherine Carroll; Nicholas Shapiro
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.033 | 0.026 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.663 | 0.012 |
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; both teacher heads agree on what is shown here.
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".