Theory, explanation, and understanding in management research
Bibliographic record
Abstract
Theory production has been a central focus of management research for decades, mostly because theory legitimizes both management research and, through its application, management practice as professional endeavors. However, such an emphasis on theory glosses over one of its constraining and particularized roles in scientific explanation, namely that theory codifies predictive knowledge. Committing to a ‘traditional’ or ‘critical’ understanding of theory thus amounts to embracing the view that prediction is achievable within a circumscribed field of study. Such an embrace is non-controversial in natural science. However, within the realm of management studies, it necessitates and smuggles in a strawman view of human existence, one which does not accommodate freedom and responsibility. This limitation of management theory explains its inadequate utility. This article argues that alternative avenues for management research exist. JEL CLASSIFICATION: M10
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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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.004 | 0.062 |
| Scholarly communication | 0.020 | 0.027 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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; 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".