Getting to clarity: new ways to think about strategy
Bibliographic record
Abstract
Purpose The purpose of this paper is to offer an opinion of current strategic thinking in North American organizations. By doing so, the paper presents a strategic model organizations can use that focuses on clarity. Design/methodology/approach The opinion and strategic framework was informed by authors’ research and consulting experiences over the past 10 years with leading companies across a variety of sectors. Findings Many organizations struggle with the strategic tradeoff between control, agility and risk and end up with complicated bureaucratic strategies. The strategic framework of clarity poses five questions that provides clear guidance for achieving focus. Practical implications Business leaders can apply the clarity framework to their existing strategic processes. By doing so, they can re-assess strategy and optimize their actions and outcomes to re-focus their strategic thinking in light of the new economy. Originality/value The paper offers a fresh perspective and opinion on strategy using familiar examples to executives. The clarity strategy framework provides executives with a simple but focused alternative to avoid strategic traps and learn from success and failure examples.
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 | 0.034 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.072 |
| Scholarly communication | 0.027 | 0.054 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".