“New markets must be conquered”: Race, gender, and the embodiment of entrepreneurship within texts
Bibliographic record
Abstract
The past decade has seen an exponential growth of postsecondary entrepreneurship programs. This article focuses on curriculum and training materials as they enable an analysis of the nuanced ways in which entrepreneurship and “the enterprising” are conceptualized, and how texts inform future entrepreneurs to embody the language of entrepreneurship. I situate this article within the fields of sociology, entrepreneurship education, and geography and bring a spatial analysis of race, gender, and class to a normally non‐spatial area of study. Although the enterprising discourse is perceived as race, gender, and class neutral, the management and self‐discipline required serve to legitimize a White, male, liberal, able‐bodied subject. Whiteness is also upheld through the privileging of abstract thinking, mobility, and the mapping of Other space. Meanwhile, entrepreneurship defined as the art of exploiting opportunities and as a creative destruction of space presents a very linear understanding of place, space, and community, dehistoricizing and decontextualizing entrepreneurship; and perpetuating a colonial, imperialist view of entrepreneurship which serves to uphold a universal, unmarked, white subject. This critique aims to allow for an understanding of the complexity of entrepreneurship, space, community, and subjectivity.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| 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".