Effective altruism and the dark side of entrepreneurship
Bibliographic record
Abstract
Purpose: Effective Altruism (EA) has become one of the most prominent socio-philosophical movements of recent years. EA is also facing intense scrutiny due to the business practices of some of its most prominent adherents. On the other hand, the dark triad traits of Machiavellianism, narcissism and psychopathy have been getting increasing attention in entrepreneurship research. There is growing evidence that these traits can motivate entrepreneurial intention. We therefore sought to investigate if there was a connection between the entrepreneurship discourse in EA and traits corresponding to dark triad behavior. Design/methodology/approach: Using a discursive analytic method, we investigated the discursive threads on entrepreneurship in EA over a 10-year period. Findings: While we believe EA brings a much-needed perspective to the overall debate on doing good, we found ample evidence that it might have promoted the sort of dark triad behavior which some evidence suggests can lead to financial success, but can equally lead to the type of morally bankrupt, unethical and even illegal practices of some entrepreneurs. We also discovered a somewhat temporal dimension in EA's discourse on entrepreneurship, beginning with discourse encouraging some risk taking and entrepreneurship, before moving on to discourses on the benefits of having a smart and illicit character, and ending with a focus on aggressive risk taking. Originality: The findings contribute to the still nascent debate on dark personality traits in entrepreneurship, and enriches the theoretical advancement of the field. However, our research differs from prior studies which were almost exclusively focused on the firm. Instead, we examine this phenomenon within a highly influential belief system/philosophical movement.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 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".