Addressing Economic Inequality through Management Education: Disrupting Student Attraction to the Myth of Neoliberal Meritocracy
Bibliographic record
Abstract
In this essay, we argue that economic inequality is reproduced because business students uncritically accept the neoliberal myth of meritocracy. This myth advances values and beliefs suggesting that hard work and innate talent lead to equally accessible opportunities and corresponding rewards. These ideas are embedded in the narratives (e.g., stories, exercises, cases, or guest speakers) prevalent throughout the business school but remain “hidden” to students because they are implicit rather than surfaced. We explain that these narratives are attractive to students and, because they are implicit within the curriculum, they limit business students’ abilities to make the systemic changes needed to address economic inequality. In our call to action, we propose a set of tools—literary analysis, plural vocality, and historical learning—that can disrupt this attraction and enable students to critically engage with the myth of neoliberal meritocracy. It is our opinion that a more critical outlook will raise students’ awareness to economic inequality and encourage them to ameliorate this type of inequality as they move through their professional lives.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".