Government sponsored competitive intelligence for regional and sectoral economic development: Canadian experiences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it