Urban industrial dereliction, a strategy of engagement
Bibliographic record
Abstract
This study focuses on 'Perception'--human and cultural perception--as the medium through w ich humanity understands, experiences, and interacts with the physical world. An exploration of perceptions surrounding the urban postindustrial landscape is carried out, and a philosophy which suggests that designing for the human perceptual experience should preclude functional or aesthetic design, is put forward. This philosophy will develop into a 'strategy of engagement', engaging perceptions of dereliction which many urban postindustrial landscapes harbor. The ultimate intent becomes to utilize this strategy in the transformation of perceptions from those of dereliction to perceptions that celebrate the intrinsic and poetic sense of place, which every landscape possesses. The first clue to what lies at the heart of this study: As the technological shift becomes more apparent in the early years of the next century, more and more heavy industrial sites will cease to function as dynamic parts of an organic city. The Cities that they nursed into existence and growth are now in the early stages of an industrial weaning process. Typically between ten and twenty percent of present day north American cities are zoned as heavy industrial land, (M1-3), and in the foreseeable future a significant quantity of this land will become perceptually derelict, or at best, incipient. (Abstract shortened by UMI.)
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".