Re-visiting the historic turn 10 years later: current debates in management and organizational history – an introduction
Bibliographic record
Abstract
The establishment of Management & Organizational History (MOH) emerged out of earlier calls for a ‘historic turn’ in Management and Organization Studies (MOS) and a (somewhat mooted) critique of existing approaches to the study of history in the field. While MOS was seen as universalist, presentist, and scientistic, attempts at historical analysis were seen by some, in the words of Alfred Kieser, as generally “myopic fact collecting without a method.” The inaugural editorial of Management & Organizational History went on to call for greater exploration of the different methodological (and philosophical) approaches to the study of history. Central to the first issue of MOH was a renewed call for a ‘historic turn.’ Ten years later, there is some question if the ‘historic turn’ has been fully realized or even adequately conceptualized. Nonetheless, a growing consensus around the need for a historical turn has arguably served to paper over some potentially significant differences and debates. In this special issue, we revisit the idea and progress of the notion of the historic turn in MOS through the eight contributing articles. We frame our discussion of the papers through a focus on the notion of the historic turn itself, the issue of critically rethinking MOS from an historical perspective, new turns and developments, MOH and contemporary thinking about the past and history, the performance of history, polyphonic constitutive historicism, fusions of methods and theoretical framing, and tales from the field.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".