Assessing the Inclusiveness of Built Facilities: A Case Study of Higher Education Facilities
Bibliographic record
Abstract
The purpose of this paper is to develop a quantitative appraisal model for assessing the inclusiveness of built facilities in higher education institutions. The Building Inclusiveness Assessment Score, or the BIAS, developed is used to assess the inclusiveness of the built facilities in the Hong Kong University. Design/methodology/approach ñ In this study, a comprehensive review of relevant guides and manuals in Canada, Hong Kong, Singapore, the US and the UK is conducted. Two Multiple-criteria Decision Analysis (MCDA) techniques, the Analytic Hierarchy Process (AHP) and the Non-structural Fuzzy Decision Support System (NSFDSS), are studied and compared and the latter is applied to analyse the weightings of inclusion attributes. On-site appraisals of 28 buildings in the Main Campus of the Hong Kong University are carried out and shortcomings resulting in exclusion are identified. Findings ñ Using the BIAS, the common areas of the built facilities in the Hong Kong University are appraised. As suggested by the preliminary result, the built facilities are not fully inclusive and there are ample rooms to improve their inclusiveness. Areas that require immediate attention are those related to the access and the safety of persons with disabilities (PWDs). Originality/value ñ Access audit and access appraisal are the methods adopted to appraise the inclusiveness of built facilities nowadays. This research seeks to obviate their shortcomings by minimising the subjective judgements of the assessors in the BIAS proposed. Compare with earlier studies in the subject, more quantitative elements are incorporated into the BIAS and its assessment procedures are detailed.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".