Bibliographic record
Abstract
The subject area of the application of information systems to small business is a thoroughly interesting, yet relatively under-researched topic. Small business is an important part of any economy. In the United Kingdom, 25% of the gross domestic product is produced by small business, which employs 65% of the nation’s workers (Ballantine et al., 1998). In Canada, 43% of economic output is accounted for by small business, employing 50% of private sector employees (Industry Canada, 1997). Further, governments view the small business sector as that component of the economy that can best contribute to economic growth (Balderson, 2000). Given the importance of this sector of the economy, it is incumbent upon researchers and managers of small business to develop a better understanding of how information systems may contribute to the operation and growth of individual businesses as well as the overall sector. The objective of this article is to provide an overview of information systems used by small business. Research projects are presented that describe the current situation. Recommendations are then proffered for various stakeholders who should contribute to a more effective use of information systems by small business.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".