Investigating collective sensemaking of a major project success
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate a case of collective sensemaking about the project success of the multifunctional amphitheater of Quebec (Canada). Design/methodology/approach For this explorative and qualitative research, the authors started from the post-mortem document and complemented their comprehension with six semi-structured interviews with the main project actors and other public documents regarding this project. Findings According to the respondents, the main success factors of this project can be attributed to: a clear governance structure; proven project management and construction methods; the use of emerging collaborative practices in construction (such as building information modeling (BIM) and lean construction); an adapted policy for procurement; as well as a code of values and ethics shared by all stakeholders. Originality/value The sensemaking perspective has been scarcely mobilized in project management studies, emerging from a constructivist view of reality and being sensitive about material-discursive practices. This exploratory study explores a case of collective sensemaking of a major project success and suggests avenues for major and megaprojects research. Lessons learned and implications for practice are also outlined. The conclusion allows a synthesis and an opening to consider how practitioners and researchers can build on this (and other successful) case(s) for future projects and research.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.032 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| 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".