Design tactics for enhancing the adaptability of primary and middle schools to the new needs of postpandemic reuse
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The aim of this study is to present design tactics (DTs) for supporting the adaptability of existing primary and middle school buildings into the emerging needs of coronavirus disease 2019 (COVID-19). The study introduces a novel algorithmic model for postoccupancy evaluation of the existing school buildings and provides solutions to enhance the adaptability of these buildings. Design/methodology/approach This study employs the DTs defined by the authors, integration of DTs to the algorithmic model and tests the usability of the proposed model in the selected sample set. The sample set consists of four primary and middle school buildings with different architectural qualities. The degrees of flexibility of the existing sample set are evaluated depending on the outcomes of the implementation. Findings The degrees of flexibility are achieved as a result of execution of the algorithmic model for each selected school building. Initial results of the case studies show that the flexibility of a school building is highly related to affordances and design decisions of the plan layout which were considered in the initial phases of the design process. Architectural qualities such as open plan and having sufficient voids in the interior and exterior space become prominent factors for ensuring flexibility. Originality/value Developing a systematic approach to the adaptation problem of primary and middle school buildings to postpandemic reuse is a novel research topic. Apart from this contextual originality, the proposed taxonomy for postpandemic reuse in terms of three levels of adaptation is a new conceptual framework. Moreover, the proposed algorithmic model itself can be considered as an original contribution, as well as a merge of qualitative values such as adaptation and flexibility with an algorithmic model.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it