Seeing power with a flashlight: DIY thermal sensing technology in the classroom
Bibliographic record
Abstract
This paper contributes to the growing literature on 'making and doing' in Science and Technology Studies (STS) by describing and theorizing the teaching of making and doing. We describe a collaborative do-it-yourself (DIY) technology project taught simultaneously in Canada and the United States, in sociology and public health, to undergraduates with no prior electronics experience. Students built thermal flashlights - low cost digital tools for making thermal images - and employed them to research their surrounding environments. By making and using the thermal flashlights, learners investigated power in two senses: identifying social power relationships embedded within normally unquestioned infrastructures, and exploring these infrastructures' connection to industrial forms of power, such as heat and electricity. Students and instructors came to understand how the control of power, light and temperature is vital to human-made infrastructure and environmental health threats that characterize the 21st century. Through this project, students went from being passive consumers of such power to become active investigators of their socio-technical systems by producing unique knowledge that enabled them to imagine how they might make and inhabit their environments differently. Breaking down the distinction between teaching and research, this article explores the promise of 'making and doing' in university courses to create new collaborative research platforms that could spread laterally and scale to transform social and technical infrastructures.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".