Exploring Collective Political Competence in Socially Complex Projects
Bibliographic record
Abstract
The involvement of many interdependent actors in projects, each with different, or even divergent, interests, creates a level of complexity that is challenging researchers and professionals alike. The aim of the exploratory research presented here is to contribute to an improved understanding of how managers can facilitate efficient operation and success for very socially complex projects. Based on the political perspective of organizations and on specific literature on the concept of competence, this research specifically focused on exploring the relevance of considering the political management of project stakeholders or project actors to be a collective competence. Results seem to support this hypothesis by providing initial confirmation of the existence of actual collective political competence, and by describing, for the first time, the contours of projects involving many participants. These results also support a more widespread theory of leadership and a more authentic approach to stakeholder management in projects as performance factors. They have also made it possible to suggest areas that would benefit from further research.
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How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".