Learning from Lafitte: An Interdisciplinary Place-based Approach to Architectural Research and Education
Bibliographic record
Abstract
An innovative trans-disciplinary research studio stack, designed to engage issues of coastal sustainability from a place-based perspective, is entering its second year at LSU School of Architecture.Pursued simultaneously through nested design studios, seminars, and independent scholarly research, these educational and research agendas are supported by the Coastal Sustainability Studios (CSS), a University-wide research initiative focusing on collaborative inter-disciplinary proposals for Coastal Louisiana.Faculty and students from the Departments and Schools of The Coast and the Environment, Earth Sciences, Renewable and Natural Resources, Engineering, Architecture, Landscape Architecture, Law, Economics, Geology, Geography and Anthropology collaborate on regional to community scale speculations throughout the lower Mississippi delta.This paper utilizes a CSS geography-based grant to test an NSF funded Long-Term Ecological Research (LTER) framework, developed to facilitate socio-ecological research, within the context of generating proposals for coupled built architectural and natural systems.By furthering the 1977 Venturi, Scott Brown, Izenour research methodology developed in "Learning from Las Vegas", through the integration of ecological and socio-cultural dynamics, time, and feedback loops (essential considerations within the dynamic deltaic system), a long-term architectural design and education research agenda that provides productive definitions of sustainability and resilience is emerging.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".