Technology adoption and gender-inclusive entrepreneurship education and training
Bibliographic record
Abstract
Purpose Drawing on social feminist theory, this paper aims to close gaps between knowledge about gender-related barriers to information, communication and technology (ICT) adoption and the provision of entrepreneurship education and training (EET) programs. Design/methodology/approach Empirical findings are drawn from 21 semi-structured interviews (22 informants) possessing differing training expertise regarding digital technology among women entrepreneurs. An open-coding technique was adopted where descriptive codes were first assigned to meaningful statements. Interpretive and pattern codes were then assigned to indicate common themes and patterns, which were reduced to higher-order categories to inform the research questions. Findings The findings specify and validate further gender influences in the digital economy. Digital skills are identified, and strategies to close gender barriers to ICT adoption with EET are described. The findings are discussed in reference to a large-scale, Canadian ICT adoption program. Research limitations/implications Perceptual data may be idiosyncratic to the sample. The work did not control for type of technology. Gender influences may differ by type of technology. Practical implications Findings can be used to construct gender-inclusive ICT supports and inform ICT adoption policies. This includes program eligibility and evaluation criteria to measure the socio-economic impacts. Originality/value The study is among the first to examine the intersection between knowledge about gender-related barriers to ICT adoption and EET. The findings can be adopted to ICT support programs targeted at small business owners and entrepreneurs.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".