Bibliographic record
Abstract
This chapter explores the role that design anthropology can play in transformative systems change through a case example, the Climate Action Lab initiative (CAL). CAL was a participative research and strategic design program commissioned by Let’s Talk Science, a national organization that provides STEM programs and resources to educators across Canada. The goals of the Climate Action Lab initiative were to: uncover the barriers that prevent youth from taking climate action, identify strategic opportunity areas for climate action programming that is relevant for youth, and offer a meaningful experience for youth to collaborate with Let’s Talk Science. Drawing on ethnographic methods and strategic foresight, the project uncovered unexpected insights about the drivers and barriers to climate action in young people, but most interestingly, the methods in and of themselves were having a surprising impact. By participating in the act of inquiry and knowledge creation, research participants were transformed into an engaged and empowered community, connected through a sense of shared impact. This finding challenged our assumptions about what climate action means and opened up entirely new pathways for imagining future engagements. In this chapter, we will make sense of this process and provide considerations for future transformative praxis.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.045 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".