Practical difficulties encountered in attempting to implement a partnering approach
Bibliographic record
Abstract
Purpose The purpose of this paper is to present practical difficulties in attempting to implement a partnering approach. Design/methodology/approach The paper comprises empirical evidence from case studies in Norway and Canada and an extensive literature review on partnering. Findings The authors identified a lack of shared understanding of key partnering concepts, missing initial effort to establish shared ground rules, communication difficulties in inter‐organizational relationships and unclear (perceived) roles and responsibilities. In existing partnering literature, a large number of construction studies have identified conceptual partnering models. However, studies that describe partnering models to take these practical difficulties into account have not been found and the paper develops a practical model that outlines the phases of a typical partnering effort. Research limitations/implications Partnering has both a legal/contractual side and a management/collaboration side. This paper looks at the management and collaboration aspects of partnering only. Practical implications The paper will be a very useful source of information and advice for project managers who are attempting to implement partnering in projects. Originality/value The paper presents organizational challenges and difficulties in attempting to implement partnering and a practical model which takes these difficulties into account.
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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 | 0.152 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".