Risk management trends in the construction industry: moving towards joint risk management
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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