Unraveling Institutional Myths: Entrepreneuring Towards Degrowth
Bibliographic record
Abstract
Institutional myths are taken-for-granted ideals that are widely held and collectively rationalized. In this paper we explore how entrepreneurs challenge one of the most prevalent institutional myths of our time – growth. To do so we conducted a qualitative study of entrepreneurs seeking to implement and design degrowth into their ventures. Drawing on social-symbolic work, institutional work and the effectuation literature, we theorize the multi-level forms of work required to challenge growth as an institutional myth. Our findings reveal that entrepreneurs generated cognitive and embodied reflexivity through awareness work, and then engaged in disruptive self-work to disentangle growth from their sense of self. We also discovered the importance of effectuating work, which we define as the design of opportunities to disengage from an institutional myth and create an alternative imaginary. We unpack these findings and contribute to the literature on institutional work, entrepreneurship, and degrowth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".