Relations intra- and inter-organisations for the study of the temporary multi-organisation in construction projects
Bibliographic record
Abstract
Construction projects are carried out by a temporary team of heterogeneous organisations called a 'temporary multi-organisation' (TMO). TMOs are constituted by procurement strategies on the part of the project client which emphasise the inter-organisation relations but which put little emphasis on the impact of internal, i.e., intra-organisational, structures, including those of the client. These procurement strategies mostly concern the contractual arrangements between the client and contractors and/or professionals. However, they do not specifically allow for anticipating the impact the participants' internal structures and relationships might have on these contractual arrangements. This article examines how the mechanisms of coordination inter- and intra-organisations influence the TMO. The research is based on the in-depth analysis of nine projects and of three institutional clients in Canada. Research findings show that formal and informal relations between project participants do not necessarily follow the legally binding procurement strategies. The findings permit identifying common patterns regarding the importance of intra-organisation relations within institutional clients, and between them and the participants of the construction industry. The patterns suggest four representative configurations of the TMO.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".