Disability inclusion studies in the construction industry: a mixed review approach
Bibliographic record
Abstract
People with disabilities are an important part of our society and are acknowledged as a wonderful piece of human variety. However, their involvement in the building business is mostly unknown. This report aims to give a critical assessment of disability inclusion studies in the construction industry and identify areas for further research. The Scopus search engine aided in the literature review search, where 253 publications were discovered using relevant keywords. After carefully reviewing the titles, abstracts, keywords, and full texts of the publications, a total of 55 relevant publications were selected. A scientometric study was undertaken to chart the landscape of disability inclusion studies within the construction industry and identify primary areas of research focus within this domain. The top five countries that are actively researching disability in the construction industry include the UK, the United States, Brazil, Canada, and Australia. The emerging areas in disability inclusion research in the construction industry focused on social value, social innovation, social procurement, employment, disability management, policies, discrimination, and disability management maturity model. The study highlighted the essential knowledge areas crucial for enhancing the integration of persons with disabilities in the construction sector to industry practitioners and other stakeholders.
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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 | 0.046 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.036 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".