Regional business intelligence: the view from Canada
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
In Canada, as is the case in most industrial countries, business intelligence (BI) has stirred much interest lately. A growing number of organizations, either large or small, nonprofit or government, implement formal BI activities. This paper provides a panorama of trends in BI in Canada. It reports research on environmental scanning, information-seeking behaviour and BI implementation and practice in large organizations and small and medium-sized enterprises (SMEs), as well as in the cultural sector. It describes governmental efforts to support disseminating and implementing BI practices especially in SMEs; in particular, the Québec Government’s Fonds de Partenariat Sectoriel Volet IV: Veilles Concurrentielles, a unique and innovative governmental programme which sponsored the development of BI centres. Finally, it provides an overview of current activities in training and research in BI. It concludes by indicating areas for improvement and development, with an emphasis on the need to develop a better understanding of information-seeking behaviour in SMEs and to develop an information model of organizations specific to SMEs.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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