Bibliographic record
Abstract
How can place be used as a pedagogy? How can place be used to deconstruct architectural education, for example, by engaging with deeper, more meaningful contexts? This begs the question: Why do we not more deliberately teach Other architectural voices and ontologies, particularly those that draw upon and listen to Indigenous perspectives about issues such as sustainability, climate change and context? This paper questions how ‘place’ can be used as a transdisciplinary methodology applied to many different fields and disciplines (including architecture, design and human geography in this paper) to contend with a myriad of complex and deeply intertwined contemporary dilemmas facing students today, ranging from post-colonialism to catastrophic climate collapse using autoethnography, phenomenology, alongside other methods such as storytelling and ‘spatialization’. The paper focuses on a particular assignment centered on the notion of place that is then tested and analyzed using three very different student groups at three very different Canadian university campuses. The background and context for this assignment is presented in terms of exploring and discussing a broad range of literature and theoretical texts to form a foundation for this exercise, followed by unpacking how ‘place’ is understood and applied to student work. A material example of a highly successful response is provided, with analysis of outcomes and approaches throughout. The goal of this paper and this ongoing project is to demonstrate how this approach and the centrality of ‘place’ presents a pedagogical alternative to traditional essays and assignments. This project directly draws upon ongoing work in critical pedagogy, decolonization, post-colonialism and vernacular and Indigenous design theories and practices.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.050 | 0.039 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".