What’s in a name? Are surnames derived from trades and occupations associated with lower GCSE scores?
Bibliographic record
Abstract
In England, there are persistent associations between measures of socio-economic advantage and educational outcomes. Research on the history of names, meanwhile, confirms that surnames in England – like many other countries – were highly socially stratified in their origins. These facts prompted us to wonder whether educational outcomes in England might show variation by surname origin, and specifically, whether surnames with an occupational origin might be associated with slightly lower average GCSE scores than surnames of other origins. Even though surnames do not measure an individual’s socio-economic position, our hypothesis was that in aggregate, the educational outcomes of a group defined in this way might still reflect past social history. In line with the research hypothesis, the results showed that the mean GCSE scores of candidates with occupational surnames were slightly lower than the mean GCSE scores of candidates with other surnames. The difference in attainment was a similar size to the difference expected between candidates half a year apart in age, and much smaller than the “gap” between male and female candidates. The explanation for the identified effect was beyond the scope of the current research, but surname effect mechanisms proposed in the literature include the psychological (e.g., implicit egotism), sociological and socio-genetic.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".