“Collective wisdom”: Inquiring into collective homes as a site for HCI design
Bibliographic record
Abstract
The home has been a major focus of the HCI community for over two decades. Despite this body of research, nascent works have argued that HCI’s characterization of ‘the home’ remains narrow and requires more diverse accounts of domestic configurations. This thesis contributes to HCI studies of domestic environments through a four-month ethnography of three collective homes in Vancouver, Canada. Collective homes represent an alternative housing model that offers agency to individual members and the collective group by sharing values, resources, labour, space and memory. This research offers two contributions. First, I offer an in-depth design ethnography of three collective homes, attending to the values, ownership models, practices, and everyday interactions observed in the ongoing making of these domestic settings. Second, I interpret and synthesize my findings to provide new opportunities for expanding the way we conceptualize and design for ‘the home’ in HCI.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".