Exploring Ed Schein’s Legacy and Enduring Influence to Inform the Future
Bibliographic record
Abstract
The contribution of Edgar H. Schein (1928-2023), to the field of management, organization studies, and applied behavioral science is both extensive and deep. For over seventy years he creatively and systematically shaped theory and practice in areas such as: organization development and change, career dynamics, the cultural dynamics of complex systems, leadership, process consultation, and the clinical inquiry/research paradigm. Following his passing on 23rd January 2023, the proposed symposium wis intended to examine and explore the way Ed’s Schein’s seminal work informs the future of the field. We proffer that “Exploring Ed Schein’s legacy and enduring future” is congruent with the 2024 meeting’s theme of Innovating for the Future. Panel Author: David Coghlan; U. of Dublin, Trinity College Author: Jean M. Bartunek; Boston College Author: Jill Waymire Paine; IE Business School Author: A.B. Rami Shani; California Polytechnic State U. Author: Baruch Shimoni; Bar-Ilan U. Author: Ilene Wasserman; ICW Consulting Group/Wharton Sr Leadership Fellow
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| 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".