Competitor Identification for Sustainable Survival Strategies: Illustration with Supply Chain Versus Supply Chain Competition
Bibliographic record
Abstract
We describe a methodology for identifying competitors from first principles, drawing on the ecological niche theory which stipulates that competition arises from the dependence of interacting entities on the same limiting resources or, in ecological terms, from overlap in their niches. Depending on the context, the entities of interest may be species, products, firms, countries, or supply chains. We discuss the concepts of niche breadth and niche overlap and provide a mathematical expression for computing the competitive effects of interacting entities on one another from niche breadth and overlap measures. We illustrate the competitor identification procedure with simulated data mimicking a situation where supply chains compete over logistics modes on which they rely for moving goods from point to point. Competition identification is invaluable to business sustainability as it allows the entities involved to remain sustainable and persist in a competitive environment by crafting effective strategies that allow them to continuously adapt to changes and mitigate the negative impacts of competition.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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 | 0.000 |
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 teacher head, 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".