The integrative domain of foresight and competitive intelligence and its impact on R&D management
Bibliographic record
Abstract
R&D takes years to come to fruition, thus choosing R&D programs should be set in the context of the environment that will exist at the time that research is completed. Foresight and competitive intelligence are two fields that seek to address future oriented environmental scanning. The paper looks at what the domains of foresight and competitive intelligence entail and in particular how competitive technical intelligence can work to integrate and enable competitive agility in foresight positioning. Focus is put on reviewing literature that addresses how foresight impacts R&D project selection. A review is made on foresight programs from around the world based on a recently completed study on Canada's foresight capacity. The authors conclude that agile organizations need to be adaptive and well prepared for tomorrow's challenges and so by integrating competitive technical intelligence, (typically oriented to business needs) with strategic technology foresight, (typically designed to address government priorities for technology investments and innovation policy issues), enterprises will be best positioned to address uncertainties in the technology cycle.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".