In praise of holistic scholarship: A collective essay in memory of Mark Easterby-Smith
Bibliographic record
Abstract
This collective essay was born out of a desire to honor and remember Professor Mark Easterby-Smith, a founder of the Management Learning community. To do this, we invited community members to share their experiences of working with Mark. The resulting narratives remember Mark as a co-author, co-researcher, project manager, conference organizer, research leader, PhD supervisor, and much more. The memories cover many different aspects of Mark’s academic spectrum: from evaluation to research methods to cross-cultural management, to dynamic capabilities, naming but a few. This space for remembrance however developed into a space of reflection and conceptualization. Inspired by the range and extent of Mark’s interests, skills, experiences, and personal qualities, this essay became conceptual as well as personal as we turned the spotlight on academic careers and consider alternative paths for Management Learning scholarship today. Using the collective representations of Mark’s career as a starting point, we develop, the concept of holistic scholarship, which embraces certain attitudes and orientations in navigating the dialectical spaces and transcending tensions in academic life. We reflect on how such holistic scholarship can be practised in our contemporary and challenging times and what inspiration and lessons we can draw from Mark’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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".