CMS: A satirical critique of three narrative histories
Bibliographic record
Abstract
Reflections on the 20th anniversary of Organization provide an opportunity for considerations of the role of the past and history in critical studies of management. Yet, why should we care? Arguably, the pages of Organization are replete with analyses that take into account the past and history. Indeed they are. However, as has been contended elsewhere, such accounts have been remarkably under-theorized for a field noted for its thoroughgoing critique of anything that moves. Nonetheless, it is not our intention to go over that ground so much as provide an appropriate example of the problem at hand. We do this through analysis of three selected accounts of how critical studies of management came into being as a field of study. Drawing on Hayden White’s approach to history, we analyse three histories of critical management studies through a focus on their respective narrative form, choosing to privilege our own narrative as satirical critique. Thus, the article does double duty by directly joining with the reflections on Organization and critical studies of management, while providing an argument for the need for greater theorization of the past and history. In the process we provide some clues to the development of the field of critical studies of management; problematize the associated notions of history and the past and make suggestions for future directions of what has become known as Critical Management Studies.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.015 | 0.031 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".