Can government CI bolster regional competitiveness?
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
Government competitive intelligence (GCI) involves monitoring and analyzing external and internal factors to support government and industry clients in their strategic and tactical decision-making. The purpose of this article is to develop a framework for exploring GCI value in a world that already has functioning private-sector CI. The framework provides a foundation for the remainder of the text discussing possible GCI clients and how they could be serviced. The precondition of value is that industry and government have intersecting competitiveness goals. The input factors that will create value are found where government and free enterprise differ in types of knowledge, access to knowledge, and resource capabilities—if there were no differences, there would be no need for GCI. Input factors include a government's unique perspectives, competencies, networks, and funding resources. GCI value is defined through improved governance and increased levels of competitiveness within a jurisdiction. © 2000 John Wiley & Sons, Inc.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.066 | 0.019 |
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