Lady, Inc.: women learning, negotiating subjectivity in entrepreneurial discourses
Bibliographic record
Abstract
This paper presents an analysis of individuals' experiential learning through their work as entrepreneurs. 1In particular, it examines women's learning as a ‘working-through’ of discursive conflicts of subjectivity. The paper is grounded in a poststructural frame that understands subjectivity to be continuously constituted through engagement with cultural discourses and learning to occur at the interstices of negotiating positionality and identity amidst contradictory discourses. The data under analysis is drawn from a qualitative study examining the learning and development of women entrepreneurs across Canada. Interviews explored the process of work learning and personal change reported by these women after at least four years running their new business, their challenges and personal needs in work, the practices they chose to engage, and their meanings of both learning and success. This analysis focuses on the discursive contexts of entrepreneurship, examining the competing images and messages which implicate women, and the various ways women business-owners learn to appropriate or resist these messages to negotiate subject positions and craft their own meanings of success and work. Implications for educators are presented at the conclusion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".