Bibliographic record
Abstract
This paper reports the results of a study conducted in 2005 across APEC economies on women-owned uptake of e-business. As reported in the APEC report, there are few statistics available on the adoption of ICT by women small business owners. Australia, for one, does not have gender disaggregated data on ICT adoption. Of the APEC economies where data is available, data is based on small samples and its generalisability to all women-owned businesses in the economy is suspect. Canada and Mexico have high percentages of women using the Internet (57% and 46% respectively). In Mexico, this is considerably higher than the percentage of total business using the Internet, which is only 7.9%, indicating the danger of a small, specialised sample (Wright, 2006). From this study it has also become evident that there has not been any attention paid in e-business policies to addressing adoption of ICT by women entrepreneurs nor to women entrepreneurs related business needs. It is proposed in this paper that women entrepreneurship, and in particular e-entrepreneurship requires an enabling environment to goes beyond e-business capacity building. We have known for some time that women are concentrated in micro and small enterprises. Many are sole proprietors and many more than men are in home-based businesses. Extensive research on women entrepreneurship has shown that women face numerous obstacles (e.g., access to finance; juggling family care and business) when venturing into business. Yet no data is available on whether this is true for e-business or not, but past experience tells us it is likely to be so. The paper introduces a coordinated and holistic empowerment model to build e-business capacity for women entrepreneurs and concludes with e-business policy implications.
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".