Heroes or Villains? Recasting Middle Management Roles, Processes, and Behaviours
Bibliographic record
Abstract
Abstract Middle management ranks are once again being questioned by scholars and practitioners alike. This introduction to the special issue represents a timely reference point for consolidating, reviving, and guiding the next wave of researchers seeking to engage this debate. We review the foundations and recent advances in middle management research and develop an organizing framework in terms of middle management's organizational roles, coordination processes, and agentic behaviours. We also identify how new ways of organizing, technology, and middle manager needs are changing to shape each of these themes. The collection of works we synthesize in this introduction offer theoretical advances and empirical evidence on how these changes affect middle management roles, processes, and behaviours. We conclude by mapping out promising research avenues for future research in middle management.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".