Psychologising meritocracy: A historical account of its many guises
Bibliographic record
Abstract
Measured by psychologists, conceived in critical terms, popularised as satire, and exploited by politicians, meritocracy is a dilemmatic concept that has changed its meanings throughout history. Social psychologists have conceptualised and operationalised meritocracy both as an ideology that justifies inequality and as a justice principle based on equity. These two conceptualisations express opposing ideas about the merit of meritocracy and are both freighted ideologically. We document how this dilemma of meritocracy’s merit developed from meritocracy’s inception as a critical concept among UK sociologists in the 1950s to its operationalisation by U.S. and Canadian social psychologists at the end of the 20th century. We highlight the ways in which meritocracy was originally utilised, in part, to critique the measurement of merit via IQ tests, but ironically became a construct that, through its psychologisation, also required measurement. Through the operationalisation of meritocracy, social psychologists obscured the possibility of critiquing meritocracy and missed the opportunity to offer alternatives to a system that has been legitimised by their own work. A social psychology of meritocracy should take into consideration the ideological debate around its meaning and value and the implications of its measurement and study.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.062 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".