Bibliographic record
Abstract
Notes on Contributors Introduction 1. Place and Edge, Edward Casey (Distinguished Professor of Philosophy at Stony Brook University, USA) 2. Place and Limit, Massimo Cacciari (Italian Institute for Philosophical Studies, Naples, Italy and the College de Philosophie, Paris, France) 3. Place and Histories - Writing Other People's Memories, Lucy R. Lippard (Free lance Writer) 4. Place and Time, Jeff Malpas (Distinguished Professor, University of Tasmania and Visiting Distinguished Professor, Latrobe University, Australia) 5. Place and Media, Joshua Meyrowitz (Professor of Communications, University of New Hampshire, USA) 6. Place and Atmosphere, Juhani Pallasmaa (Professor Emeritus, Juhani Pallasmaa Architects) 7. Place and Architectural Space, Alberto Perez-Gomez (Saidye Rosner Bronfman Professor in History and Theory of Architecture, McGill University School of Architecture, Canada) 8. Place and Connection, Edward Relph (Professor of Geography, University of Toronto, Canada) 9. Place and Sensory Composition, Kathleen Stewart (Professor of Anthropology, University of Texas at Austin, USA) 10. Place and Formulation, Kenneth White (Royal Scottish Academy, Professor of Twentieth-Century Poetics, Sorbonne, Paris) Index
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".