Adoption and Use of Computer Technology in Canadian Small Businesses
Bibliographic record
Abstract
This chapter examines the impact of company size on the adoption, use and perceived impact of computer technology in Canadian businesses. Such research is critical for several reasons. First, while there is a large body of research that examines the adoption, use and impact of computer technology, most studies either ignore workplace size or focus exclusively on medium and large workplaces. Second, most research on computer use in small business has grouped businesses of various sizes (that is under 99 employees, under 200 employees) into one category for study, with the assumption that all small businesses have similar computing applications needs and adoption practices (e.g., Malone, 1985; Nickell and Seado, 1986). Third, our previous research with small businesses would suggest that owners of Canadian small businesses are becoming more interested in computer technology (Duxbury and Higgins, 1999). Finally, research in this area is critical because of the increasing economic importance of this sector to the Canadian economy.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".