on the determinants of a successful, sustainable-driven adaptive reuse: A multiple regression Approach
Bibliographic record
Abstract
The purpose of this paper is to outline an ongoing research, examining the determinants of a successful, sustainable-driven development.The practice of adaptive reuse is connected with sustainable development and although it is widely believed that mainly economic factors drive possible development schemes, it is found through this research that, in the case of adaptive reuse, there are some other contributing criteria.The methodological tool implemented to obtain the results is multiple regression analysis and the contributions included in the model are based on the fields of socio-economics, culture and the environment.These vital contributions are key components of both the practice of adaptive reuse and sustainable-driven developments of the built environment.The advantage gained by applying statistical methods to examine multi-criteria cases is the possibility for well-justified observations; these are intended to be valuable tools for decision makers and involved stakeholders aiming to achieve successful sustainable adaptations.although the findings presented in this paper are derived from research data collected in cyprus, the methodological approach could be applied to a broader context, hence leading to more universal conclusions.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".