Collaborative academic/practitioner research in project management
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide of a review of the theory and models underlying project management (PM) research degrees that encourage reflective learning. Design/methodology/approach Review of the literature and reflection on the practice of being actively involved in conducting and supervising academic research and disseminating academic output. The paper argues the case for the potential usefulness of reflective academic research to PM practitioners. It also highlights theoretical drivers of and barriers to reflective academic research by PM practitioners. Findings A reflective learning approach to research can drive practical results though it requires a great deal of commitment and support by both academic and industry partners. Practical implications This paper suggests how PM practitioners can engage in academic research that has practical outcomes and how to be more effective at disseminating these research outcomes. Originality/value Advanced academic degrees, in particular those completed by PM practitioners, can validate a valuable source of innovative ideas and approaches that should be more quickly absorbed into the PM profession's sources of knowledge. The value of this paper is to critically review and facilitate a reduced adaptation time for implementation of useful reflective academic research to industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.124 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".