Bibliographic record
Abstract
Purpose This paper reviews the latest management developments across the globe and pinpoints practical implications from cutting‐edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings Back in the late 1800s, John Abbott said that “every man's ability may be strengthened or increased by culture”. Well over a century may have passed since then, but time has not dulled the significance of the former Canadian prime minister's words. Just ask the folks at Walt Disney. Mere mention of the entertainment giant's name invariably conjures up memories of lovable characters and unparalleled fun for the young and not so young alike. At the company itself, however, fun seemed no longer part of the equation. And the reason for this? The prohibitive culture that soured boardroom relations. Under autocratic former CEO Michael Eisner, control rather than collaboration was the norm and unit heads became afraid or unable to make decisions. With Disney vying for a share of the digital market, the timing of the upheaval could hardly have been worse. Talk about pressing the self‐destruct button. Practical implications Provides strategic insights and practical thinking that have influenced some of the world's leading organizations. Originality/value The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy‐to‐digest format.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.033 | 0.012 |
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".