Collaborative academic/practitioner research in project management
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a cost‐benefit interpretation of academic‐practitioner research by describing and analysing several recent relevant examples of academic‐practitioner research with a focus on doctoral theses carried out at universities and business schools in clusters of research centred in North America, Australia and Europe. Design/methodology/approach Using case study examples, a value proposition framework for undertaking collaborative research for higher degree level study is developed and presented. Findings Value proposition benefits from this level of collaborative research can be summarised as enhancing competencies at the individual and organisational level as well as providing participating universities with high‐quality candidates/students and opportunities for industry engagement. The project management (PM) professional bodies can also extend PM knowledge but they need to be prepared to provide active support. Practical implications A model for better defining the value proposition of collaborative research from a range of stakeholder perspectives is offered that can be adapted for researchers and industry research sponsors. Originality/value Few papers offer a value proposition framework for explaining collaborative research benefits. This paper addresses that need.
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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 | 0.134 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".