The meritocratic illusion: inequality and the cognitive basis of redistribution
Bibliographic record
Abstract
Abstract Can an inequality in rewards result in an erosion of broad-based support for meritocratic norms? We examine whether unequal rewards can affect social preferences for redistribution by driving a cognitive gap in the meritocratic beliefs of those who are successful and those who are not. Two separate experiments (conducted in the USA and the UK) show that the elite develop and maintain ‘meritocratic bias’ in the redistributive taxes they propose. This bias results in lower taxes on the rich and fewer transfers to the poor, including those who failed despite high effort. These social preferences at least partially reflect a self- serving meritocratic illusion that their own high income was deserved and reflected their ability. An incentivized Wason Card task confirms that individuals prefer to maintain their illusion of being meritocratic, by not expending cognitive effort to process any information that may undermine their self-image of being deserving.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| 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".