Reflections on the Canadian Government in competitive intelligence – programs and impacts
Bibliographic record
Abstract
Purpose This paper aims to present a categorization scheme and use it to classify Canadian Government (federal and provincial) competitive intelligence (CI) programs and to also look at the impact of these programs on sectoral and regional economic development. Design/methodology/approach Based on the author’s 25 years of experience designing, running, and studying Canadian Government CI programs, a classification scheme to classify these programs has been developed and used. Also, by using program review information, this paper looks at evidence for program impact on regional and sectoral economic development. Findings This paper identifies a broad range of federal and provincially sponsored CI programs aimed at helping both government officers and those outside the department make better decisions. The review identified several roles that the government can play in using CI: creator of CI (both for their own purposes and also for helping Canadian companies), CI environment skills builder (helping Canadian companies develop skills in developing their own CI) and CI partner (working jointly with Canadian companies in developing CI). While there have not been many formal program reviews of the CI programs sponsored by Canadian Government departments and agencies, anecdotal evidence (from training program participant evaluations) and a comprehensive review of a small community CI-based economic development program support positive sectoral and regional economic development results arising from these programs. Practical implications CI programs can be used as part of a government’s regional and sectoral economic development approach. CI can be used to assist with decision-making both within and outside the government. This paper identifies several different kinds of programs that can be used to further a government’s economic development agenda. Originality/value There are very few articles that examine how governments have helped companies to develop CI and how they have used CI, and none has looked at the impact of these on regional and sectoral economic development. This paper, based on the author’s experiences, provides a view of the Canadian programs and their impact on regional/sectoral economic development.
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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 | 0.025 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".