Progress or pinkwashing: who benefits from digital women-focused capital funds?
Bibliographic record
Abstract
Abstract This paper examines the positioning of gender within women-focused capital funds (WFCFs) to consider the extent to which these digitally enabled sources of finance reflect the tenets of entrepreneurial feminism. Content analysis of 27 funds situated in Canada and the USA informs about fund mandates, rationales, types of capital, and anticipated outcomes. Our findings reveal that a minority of WFCFs examined sought to enhance equity and counter structural barriers associated with women entrepreneurs’ access to financial capital. Alternatively, the majority of WFCFs were positioned as vehicles to facilitate individual wealth creation. Eligibility ranged from multiple gender identities of the business owner to “women-led” businesses—defined as at least one woman executive, board or steering committee member. The latter of these criteria has the effect of diverting attention away from firms that are launched by women entrepreneurs. Pinkwashing was more likely to occur when WFCFs were created as add-ons to mainstream programs and services, rather than as a central element of the organization’s mission of supporting women and non-binary femmes. The findings support arguments that technology can both challenge or reinforce structural constraints that impede women entrepreneurs in the digital era.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".