Doing Impactful Research in Organization and Management Theory: Taking Stock and Retooling
Bibliographic record
Abstract
Doing impactful research has become the new currency in the field of organization and management scholarship. Impactful research is generally understood as the development of actionable insights in relation to important phenomena. This recent trend has already had a variety of positive outcomes, from channeling the attention of the academic community towards important topics to providing actionable insights. However, some scholars are urging to push this agenda further, stressing the limitations of the theories, perspectives and methods employed, and challenging the very notion of impactful research. To critically reflect, discuss, and accumulate knowledge about conducting impactful research, our symposium assembles a panel of five distinguished scholars from diverse disciplines, all committed to doing impactful research. In their work, they have highlighted the limitations of the main concepts, theories, and methods that their disciplines have to offer when conducting impactful research. Importantly, they offer alternatives to go beyond the current scope of impactful research and harness its full potential. As such, the symposium aims at developing a generative research agenda on how to conduct impactful research. The feasibility of this agenda in the current academic system, particularly for early career scholars, will be discussed.
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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 | 0.156 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.018 | 0.125 |
| Scholarly communication | 0.041 | 0.057 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.008 | 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, 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".