The Possibility of the Virtual Focus Group: Communicating Agency Toward Equitable Participation Beyond the COVID-19 Pandemic
Bibliographic record
Abstract
The modes of design practice are shifting in light of the COVID-19 pandemic. We now learn, research, and teach through virtual platforms more integrally and intensively than ever. This moment begs for introspection and the reconsideration of ‘conventional’ workflows in the discipline of architecture. The authors of this paper are members of an interdisciplinary research team who discuss how they adapted their research methodologies with a virtual toolkit, developing focus group sessions with multi-family building residents and graduate students. The authors reflect on the benefits and limitations presented by digital tools and consider how hybridized opportunities suggest tailored approaches that facilitate the communication of agency to a representative and complex public. Participatory design frameworks ground the conversation, allowing the authors to position their methodology as an essential step to establishing equitable grounds for participation in the future.
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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 | 0.122 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.040 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".