Government sponsored competitive intelligence for regional and sectoral economic development: Canadian experiences
Bibliographic record
Abstract
Can competitive intelligence (CI) be used to assist in regional and sectoraleconomic development? This article looks at intelligence initiatives (largely around training)sponsored by various government departments and agencies in Canada and their link toregional and sectoral economic development. The article provides examples of the kind ofintelligence initiatives that have been used in Canada to support regional and sectoral(industrial) economic development. The article proposes a method for categorizing theseregional and sectoral intelligence programs and suggests methods for assessing the impact ofthese programs on regional and sectoral economic development. The Canadian programs aredivided into three broad categories 1) Government programs aimed at enhancing their ownability to develop competitive intelligence 2) Programs that are sponsored by the governmentfor industry and others to develop competitive intelligence and 3) Programs sponsored by thegovernment to help communities develop competitive intelligence for local economicdevelopment. Positive economic impacts were identified using program review documents,government officer reports and anecdotal evidence from program participant surveys. However,while the evidence does support positive impact a more comprehensive approach to evaluatingthese impacts should be considered in the future.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".