The Research-Practice Gap in Management Field: A Relational Approach Based on the Quintuple Helix Model
Bibliographic record
Abstract
The academic literature in the field of Management is often criticized because of its little relevance to the professional community. The literature maintains that a considerable gap has been created between research and practice in this discipline, suggesting the need to develop more literature that can unite both communities. A sizeable body of literature has been developed with the objective of analysing and proposing different remedies and solutions to this important problem. In this work, we offer a vision of the current state of literature in this topic and the different currents that have focused on proposing solutions to this problem. Thus, we guide the readers towards understanding the existing gaps and how each approach aims to fill them by your study. Moreover, drawing on them, we propose the theoretical perspective of the Quintuple Helix as a relevant perspective to describe the relationships between professionals and academics, and how the disconnections between them can be managed. Therefore, this work contributes to the existing literature with the proposal of the Quintuple Helix model as a framework to establish a communicative structure and stronger links between both communities.
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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 | 0.046 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.009 | 0.064 |
| Scholarly communication | 0.026 | 0.045 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".