Continuous reconfiguration in the transient advantage economy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose – The author identified a set of companies that have figured out how to cope, even to thrive, within the new transient advantage economy and explains how they did it. Design/methodology/approach – Her research team analyzed nearly 5,000 companies. Of that whole population, only ten companies were able to grow their net income by at least 5 percent a year for ten years in a row. These ten “outliers” have out-performed competitors while adapting to rapidly changing market forces. Findings – Organizations that have mastered transient-advantage environments have learned to continually free up resources from old advantages in order to fund the development of new ones. Additionally, innovation is continuous, mainstream and part of everyone's job. Research limitations/implications – The 5,000 companies analyzed included every publicly traded firm on any stock exchange with a market capitalization of over $1 billion. The article studies the practices of the ten most successful over ten years. Practical implications – The most successful firms, over the entire study period, had no dramatic downsizings, restructurings or sell-offs. Originality/value – The author found that the key leadership and management challenge is maintaining an organizational system that can manage the complementary forces of innovation and stability.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it