The execution trap. Drawing a line between strategy and execution almost guarantees failure.
Bibliographic record
Abstract
The realization of a strategy depends on countless employees. So it's no surprise that when a strategy fails, the reason cited is usually poor execution. But this view of strategy and execution relies on a false metaphor in which senior management is a choosing brain while those in the rest of the company are choiceless arms and legs that merely carry out the brain's bidding. The approach does damage to the corporation because it alienates the people working for it. A better metaphor for strategy is a white-water river, in which choices cascade from its source in the mountains (the corporation) to its mouth (the rest of the organization). Executives at the top make the broader choices involving long-term investments while empowering employees toward the bottom to make more concrete, day-to-day decisions that directly influence customer service and satisfaction. For the cascade to flow properly, a choice maker upstream can set the context for those downstream by doing four things: explaining what the choice is and why it's been made, clearly identifying the next downstream choice, offering help with making choices as needed, and committing to revisit and adjust the choice based on feedback. When downstream choices are valued and feedback is encouraged, employees send information upward, improving the knowledge base of decision makers higher up and helping everyone in the organization make better choices.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 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".