Female entrepreneurs’ identity responses to mumpreneur categorisations in the media
Bibliographic record
Abstract
This paper examines how female entrepreneurs labelled as mumpreneurs are constructed in public discourse and analyses identity work by women themselves as they respond and discuss the mumpreneur label with each other in online social spaces. Using the theoretical perspective of social categorisation and its identity effects (Jenkins, 2000; Watson, 2008), we analyse data from newspaper articles about mumpreneurship from the UK, Ireland, the US, Canada, Australia and New Zealand, and data from online discussions between mumpreneurs in the same six countries. We show how mumpreneurship is narrowly constructed in public discourse as a form of motherhood that involves combining ‘pin-money’ type economic activity with performance of intensive mothering norms and is distinguished, often negatively, from other forms of ‘real’ business activity and entrepreneurship. We show how women who feel categorised as ‘mumpreneurs’ create identity in response to this categorisation in three ways: 1.) adopting and celebrating the mumpreneur label; 2.) defining themselves by rejecting and resisting the mumpreneur label; or 3.) striving for autonomy of self-identification. The contribution of this paper is to create new insight into how mumpreneurship as an identity project is pursued at the intersection between external social categorisation and personal identity work in the context of gendered discourses of entrepreneurship and norms of intensive mothering.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| 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".