‘The association between intelligence and lifespan is mostly genetic’
Bibliographic record
Abstract
We were sadly disappointed to read the recently published article by Arden et al.1 (in press) in the International Journal of Epidemiology . This article is one that we believe to be unsound in its conceptualization and execution and which should not have been considered for publication in an epidemiology journal even if it did not suffer from these methodological flaws. The scientific hypothesis pursued by these authors is that individuals with higher intelligence quotient (IQ) have longer lifespans, that this relationship is due primarily to common causes of both variables and finally that the common causation is primarily genetic rather than environmental. They investigated these relationships in three cohorts of aged same-sex monozygotic and dizygotic twin pairs. There are a number of statistical analyses reported in the paper, and these violate many widely accepted principles of epidemiological analysis and reporting, such as the avoidance of standardized effect estimates 2 and the reliance on null hypothesis significance testing rather than reporting of effect estimates and their imprecision. 3 The paper makes inferences about genetics and environments, but has no direct measures of either set of variables. Rather, the key assumption on which the inference rests is the ‘equal environment assumption’ (EEA), which is that twins are not exposed to different environments based on their zygosity. This assumption is stated by the authors as a fact, but is not evaluated in these data. When evaluated in previous reports it is sometimes reported to hold approximately, 4 and at other times found to be severely violated. 5
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.036 | 0.051 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".