Self-criticisms toward a socially responsible science in the field of management
Bibliographic record
Abstract
Management scholarship should be placed in a unique position to develop relevant scientific knowledge because business and management organizations are deeply involved in most global challenges. However, different critical voices have recently been raised in essays and editorials, and reports have questioned research in the management field, identifying multiple deficiencies that can limit the growth of a relatively young field. Based on an analysis of published criticisms of management research, we would like to shed light on the current state of management research and identify some limitations that should be considered and should guide the growth of this field of knowledge. This work offers guidance on the main problems of the discipline that should be addressed to encourage the transformation of management research to meet both scientific rigor and social relevance. The article ends with a discussion and a call to action for directing research toward the possibility and necessity of reinforcing “responsible research” in the management field. JEL CLASSIFICATION: M00, M10
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 | 0.145 | 0.280 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.011 | 0.097 |
| Scholarly communication | 0.032 | 0.021 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".