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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".