Bibliographic record
Abstract
As architects, professionals, and academics, we constantly operate at a minimum. This isn’t to say that workloads are small, or that stress is minimal – absolutely not – but industry and institutional requirements are based largely on minimum requirements. The minimum has become, in effect, a concep¬tual framework for how we think, do and teach architecture), as well as the econo-speak of “efficiency” that characterizes much of our institutional (and disciplinary) processes and structures – limiting and (minimizing) teaching and research and real-meaningful service. “Norms” and “minimums” of education and practice are established for accountability – but this prioritizes and shifts accountability to governing bodies and institutions (who by and large, serve to replicate themselves, and usually, extrac¬tively). How do we resist this, and create a maximum space where faculty, students and staff can recognise and see them¬selves in curricula, research and teaching spaces? The paper investigates what it means to ask for institutional dissent, and to ask for more (and engaging in going beyond the minimum) when considering what guides our architectural education. Reflecting on a series of scales (Small, Medium,Large, Extra Large) of doing meaningful equity based work, this paper reflects on efforts at Carleton University’s Azrieli School of Architecture and Urbanism in Ottawa/Canada, in an attempt to think through, , explore and develop an architectural infrastructure of empathy. We see this work as a field guide (emerging though it is) for how we can begin to demand more from those who set out, and those who uphold, the minimums. What follows in this paper is a narrative on scale, on steps (some missed, some struck, and some possible), of an emerging framework around an academic episteme and practice of care.
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 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.251 | 0.175 |
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".