Unmaking: Exploring Agency Through Unmaking
Bibliographic record
Abstract
This thesis proposes that if people and local communities were more skilled in making and repair, they could be more resourceful with the objects around them, making it possible to engage in more sustainable practices. Such skills afford a revised pattern to the consumption of products, services and materials. The thesis explores an observed gap between a person’s sense of agency and their capabilities to act in more sustainable ways. Maker movements, Transition Towns, and other project-based learning organizations like Vancouver’s Citystudio and Costa Rica’s Earth University, are re-skilling people to live more sustainable lives. Communal learning and tangible skills build more self-reliant communities. These movements are seen as vital steps in a long path toward sustainable local and circular economies. Through a series of hands on ‘Unmaking’ workshops the research attempts to leverage our relationship to waste electronics and appliances as mode of exploration to discuss ideas of agency, capability and curiosity. By taking waste electronics and appliances apart, un-boxing the black-box, participants mindfully investigate our complicity in their existence, and ultimately develop new understandings and skills to collaboratively tackle their adverse effects. The act of Unmaking, not only provides a platform for discussion, but also gives participants an opportunity for co-learning driven by mutual curiosity. The heuristic nature of this research opens up an exploratory space for designers and non-designers alike that encourages a reflective practice. The resistance to adopt more sustainable lifestyles partly lies in a lack of understanding of our built environment, the resources and energies involved in its production, and a sense of value in the objects we encounter in our daily lives.
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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.048 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".