Bibliographic record
Abstract
If your company develops and tests strategies to outmaneuver the competition, do you fall into the trap of focusing on just traditional rivals, or worst, on just their historic strategies? To avoid this danger, use an innovative approach that several leading companies have adopted: create an “invented” competitor and then consider what type of strategy the invented competitor might employ. Using “invented competitors” opens up a view into the future that is likely to challenge the preconceptions and obsessions of your current worldview. This approach can identify new business opportunities and surface limitations and vulnerabilities in your company’s current strategies and operations. For a number of leading organizations, the invented competitor is proving to be a spur to bold and innovative thinking. An invented competitor is a rival that could exist some time in the future and has two related features that current rivals do not have: unique marketplace strategy and organizational configuration (two examples: a different network of relationships or value chain structure). Invented competitors help managers to reflect about the future in novel and insightful ways. Although the process of inventing a competitor varies, depending upon the context and purpose, it typically has five elements: how the competitor might come to be; what its strategy might be; how it might execute its strategy; why it might win or fail; and the strategy implications for our firm.
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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".