Control and traceability of research impact on practice: reframing the ?relevance gap' debate in management
Bibliographic record
Abstract
This paper aims at reframing the relevance gap debate in management scienceby repositioning scholar-practitioner collaboration and knowledge coproduction practices regarding knowledge relevance and impact. Based on a reflection about the nature of management knowledge, we argue that the so-called relevance gap should be more aptly reframed as a ‘traceability’ or a ‘controllability’ gap. Although management knowledge may be deemed relevant by a wide range of practitioners, the ways these practitioners use management knowledge are hardly visible, let alone controllable. Scholar-practitioner collaboration can be seen as a way for management scholars to regain some control over the utilization process, rather than a way to ensure knowledge relevance as such. Instrumental knowledge, which is paramount in the popular design-science perspective, certainly accounts for a share of management knowledge. Besides this, the design-science perspective offers a promising way to put scholar-practitioner collaboration into practice. It enhances the visibility of research products and the traceability of knowledge transfer. Yet instrumental knowledge should not be seen as the only type of relevant and used knowledge. Conceptual and critical knowledge are vital for management science. Instrumental relevance should be complemented by conceptual relevance, although the latter seriously tempers scholars’ quest for traceability and control over knowledge utilization. In the debate about the relevance and impact of management knowledge, the fundamental question of ‘knowledge for whom?’ should remain at the center of the debate.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.285 | 0.319 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.012 | 0.136 |
| Scholarly communication | 0.043 | 0.075 |
| Open science | 0.007 | 0.037 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".