Riding the wrong wave: Organizational failure as a failed turnaround. Long Range Planning 38
Bibliographic record
Abstract
This article proposes that a failing organization goes through a sequence of four stages before finally landing in the morass of death. A four-stage model is proposed to describe this journey, which can lead to failure or to turnaround. By categorizing the elements of failure or turnaround, the model explains how the elements germane to each stage, when combined, facilitate the progression of an organization from crippling deterioration in performance to eventual death or to re-stabilizing survival. To support our contention, we focus on the Canadian retail industry, and specifically on the story of the once very successful but now extinct merchandising icon T. Eaton Co. Ltd., contrasting its fortunes with those of fellow Canadian retail survivors Hudson’s Bay Company and Canadian Tire. 2005 Elsevier Ltd. All rights reserved The current rage for courses of study in North American universities is crime scene investigation. Based on the popular U.S. TV show CSI (Crime Scene Investigation), students are applying in vast numbers to study forensics, and a number of schools that do not offer this course of study are working on developing it.1 It seems that people are far more willing to study the rather squeamish matter of vicious crime and even murder than to look at the somewhat
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.051 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".