Gender, disadvantage and enterprise support – lessons from women's business centres in North America and Europe
Bibliographic record
Abstract
Purpose The aim of this paper is to analyse support measures in the USA, Canada and Sweden aimed at encouraging women to start their own business and/or promote growth in women‐owned businesses, and in particular the role of women's business centres. It examines whether existing initiatives of this kind have proven successful in their stated and unstated aims; and if elements of practice are transferable to other countries and contexts. The paper also contributes to the gender mainstreaming debate. Design/methodology/approach Through in‐depth interviews across four countries with managers of such centres and other business support personnel, policy‐makers and practitioners, the paper constructs a view of how women's business centres fit into the overall policy context, and how they have aided the development of women's enterprise. Findings The use of international comparisons permits the identification of common approaches to enterprise policy for women. Policy‐makers and practitioners will appreciate the nuanced view of the elements that make up several lauded initiatives aimed at supporting women's entrepreneurship, how (and to what degree) these elements work together and how these elements may be used elsewhere. Research limitations/implications The paper suggests the need for more nuanced understanding of client needs, whether male or female, and the role this might play in the delivery of business support. Practical implications Policymakers should be clear regarding the objectives of women's centres, as between support principally directed at unemployed/low income groups and increasing the business start‐up rate per se among women (leading to economic growth), and even whether support should be differentiated by gender. Social implications Women's centres are working mainly for the more disadvantaged women, rather than those with real potential as entrepreneurs. Such centres may also reinforce stereotypes of “women's businesses”. Originality/value The key contribution of this paper is that, compared to previous work, it provides a more critical perspective on the specifics of women's business centre initiatives, exploring both the processes and outcomes that lie behind the simple output‐related success measures that often characterise mainstream policy evaluations.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".