Fashioning ethical subjectivity: The embodied ethics of entrepreneurial self-formation
Bibliographic record
Abstract
Organizational ethics has attracted increasing attention, but how individuals make sense of themselves as ethical subjects is a yet to be explored domain. The few empirical articles on ethical subjectivity have focused on how people within organizations seek to find a balance between a sense of ethical selfhood and dominant organizational discourse. We are interested in the role of the body and embodied experiences in constructing the entrepreneurial self and how this process unfolds over time. Viewing entrepreneuring as an ethical practice, we rely on a larger study of 58 entrepreneurs and a smaller multi-modal ethnography of three entrepreneurs in the ethical fashion industry. Drawing on the Deleuzian four folds of subjectivity that we employ as an analytical device, the data analysis reveals how our protagonists use the body as sensor, source, and processor in constructing themselves as ethical subjects. Our study complements rational perspectives on ethical decision making in entrepreneurship and establishes the body as a primary mechanism for one’s formation as an ethical subject. Through connecting the body with ethics, we aim to disclose the continuous subtle interaction between morality and materiality in the process of entrepreneuring. Our abductive framework discloses how one’s body prompts and informs the development of moral actions and material artifacts.
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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 | 0.008 | 0.013 |
| 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 | 0.038 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".