Risk management trends in the construction industry: moving towards joint risk management
Bibliographic record
Abstract
This paper reports the outcomes of the first of three planned questionnaire surveys in the first phase of a broader Hong Kong based study on ‘Joint Risk Management’ (JRM). The survey compared perceptions on both present and preferred risk allocation, including JRM, in construction contracts. Data was mainly collected in Hong Kong and mainland China (with most respondents having working experience from Hong Kong) from various professionals and practitioners representing broad groups of academics, consultants, contractors and owners (clients). Survey results reinforce previous observations (in Canada) of the divergences in perceptions on both present and preferred risk allocation, both within and between different contracting parties. The present study reveals quite wide (marked) divergencies with many individual cases of diametrically opposing views on allocating particular risks within specific groups. Despite such divergencies, respondents professed a general enthusiasm towards JRM, irrespective of their contractual or professional affiliation. Moreover, they generally preferred to assign reduced risks from either one or both contracting parties to JRM, rather than shifting more risks to the other party. This is indicative of a perceived trend towards more collaborative and teamwork based working environments.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".