The rigor-relevance gap in Project Management research: It's time to stop the lament and think and act reflexively
Bibliographic record
Abstract
Over the last decades, the "rigor-relevance gap" has garnered attention in Project Management (PM) research. In this paper, we argue that reflexivity can help produce more relevant and rigorous research and we invite scholars to stop sitting on the sidelines only to lament such a gap. In our clarion call to overcoming the gap, we challenge scholars to take an active and competent part. To that end, we outline typical reflexive questions along with four main pillars that scholars ought to take into account: 1) the status of the PM knowledge field; 2) the evolution of PM through historical periods of thoughts; 3) PM and social theory; and 4) ontological and epistemological assumptions. We also showcase the dialectical interplay not only between sociology and PM but also among these interdependent pillars. Finally, we conclude that such an interplay offers the best opportunity to overcome the rigor-relevance gap in PM.
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 | 0.397 | 0.408 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.011 | 0.134 |
| Scholarly communication | 0.043 | 0.058 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.016 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".