Post-industrial landscapes as renaissance<i>locus</i>: the case study research method
Bibliographic record
Abstract
The fact that several countries are now facing various problems produced by landscapes constructed during the modern period [e.g.industrial revolution], currently in complete physical and functional decadency, contributed to enlarge the negative public perception about these spaces.However, this perception associated with the need to protect the environment has been in the last decades the catalyst to the redevelopment and renaissance of these landscapes.Often in advantageous locations near city centres, situated along waterways, supported by existing infrastructure, and adjacent to residential communities, these landscapes are environmentally impaired assets that need to be returned to productive uses, and reintegrated into the surrounding community.The reclamation and conservation of these landscapes constitute, additionally, an important cultural objective, which is inherently sustainable in that it encourages the positive re-use of redundant buildings that are part of our industrial and commercial heritage.This paper addresses the urgent need to reclaim these landscapes, influenced both by two different tendencies connected with the abandonment of industrial landscapes: on the one hand, the urban pressure related to the city's administration and stakeholders' will to urbanize those areas and on the other hand, the increasingly public awareness of the necessity to protect industrial heritage.This paper presents an approach based on the case study research method.This approach and the way it is applied in this paper may be empirically described as the research and analysis of several successful post-industrial landscape reclamation design approaches, in order to build a set of design principles that might inform and serve as a basis to the redevelopment of similar landscapes.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".