The Smart Fan; An Actor-Network Theory Analysis of the Montreal Protocol
Bibliographic record
Abstract
Over the past century, air conditioning (AC) systems have evolved from a novel invention to a widespread household necessity in many developed countries. A cooled environment provides numerous benefits; however, the popularity of AC technology raises environmental concerns. In this portfolio I explore ways to improve the environmental sustainability of air conditioning through two related projects. For modern AC systems, energy consumption has been a primary concern. Therefore, one avenue of improvement is replacing or supplementing conventional AC systems with energy efficient alternatives. To make alternatives to AC more appealing, I will present a design for “The Smart Fan”, a portable desk fan that automatically re-orients itself to blow air on a user as they move. This device aims to combine the efficiency of an electric fan with some of the convenience of AC. Working on a team with three other students, I will develop and test a prototype of The Smart Fan. The evolution of cooling technology is dependent not only on engineering work, but on the sociotechnical networks that facilitate their use. Therefore, in the STS project, I will consider the global phase-out of CFCs as a case study in the adoption of more sustainable cooling technologies, using Actor-Network Theory to analyze the processes leading to its success. Together, these complementary projects will shed light on the technical and social aspects of cooling technology, both of which must be addressed to reduce its environmental impact.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".