When Competitive Intelligence Meets Geospatial Intelligence
Bibliographic record
Abstract
Given current economic uncertainties, it is important for enterprises to efficiently generate new knowledge and apply it in their products and services. In this regard, there is at least one source of assets that remains underutilized: data. An enterprise may be able to improve its competitive position by developing a better understanding of the value of its data, and competitive intelligence may prove very useful in this regard. Our study sought to identify, among some 40 competing analytical methods, those that may be enhanced by geospatial intelligence capabilities. We began by identifying the key criteria and dimensions and then determined the most promising approaches. Our results demonstrate that geospatial intelligence may serve competitive intelligence by improving and integrating certain dimensions of competitive analyses, and, in the process, efficiently identify business opportunities.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".