The past masters: the impact of the <i>Evolution of Management Thought</i> on history
Bibliographic record
Abstract
Purpose The purpose of this article is to commemorate the 50th anniversary of Evolution of Management Thought (EMT), a critically acclaimed text in management and organizational studies for its value in historicizing the practice of management. Design/methodology/approach The authors asked Daniel Wren and Arthur Bedeian in their own words to their contribution. In addition, the authors offer commentary and critique of 16 leading management historians who share their reflections on the intellectual significance of Wren and Bedeian, and the punctuation of EMT as a canonical text in the field of management history. Findings The legacy of Wren and Bedeian can be felt across the academy of historical research on business and organizations. Their work has separately made significant contributions to management studies but together they have forged a fruitful partnership that has given rise to multiple generations of scholars and scholarship that continue to shape the field to this day. Originality/value The contribution of the authors in this article is to mark the significant milestone of EMT’s five-decade success by hearing from the authors themselves about their longstanding success as well as giving space to critique about the past, present and future of our collective historical scholarship shaped by Wren and Bedeian’s legacy.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.053 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".