Design tactics for enhancing the adaptability of primary and middle schools to the new needs of postpandemic reuse
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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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".