Aesthetics, well-being and health : essays within architecture and environmental aesthetics
Bibliographic record
Abstract
Part 1 Introduction: aesthetics, well-being and health. Part 2 The essays: enclosure and structure, David Canter beauty, Birgit Cold the aesthetics of place, Kim Dovey creating aesthetic built environments through the user participation process, Aase Eriksen what happens if Zeleste becomes an architect?, Arnulf Kolstad the architectural psychology box of infinite knowledge, Rickard Kuller housing, health and aesthetics - reconnecting the senses, Roderick Lawrence chuck out the chintz?, some observations on aesthetics, well-being and health, Sue-Ann Lee reflections on concepts of aesthetics, health and well-being, Byron Mikellides aesthetics in the built environment and its influence on the user, Kaj Noschis the mind of the environment, Juhani Pallasmaa aesthetics, order and discipline, Jens Scherup Hansen I live in a beautiful house, on a beautiful street in beautiful Montreal -notes on well-being and the experience of place aesthetics, Perla Serfaty-Garcom environmental aesthetics and well-being -implications for a digital world, Daniel Stokols reasonable persons and their aesthetic preferences, Einar Strumse conversations on aesthetics, David Uzzel buildings imagined as bodies, Ann Westerman appendices.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".