Assessing developmental predictions of life history theory in psychology using census data
Bibliographic record
Abstract
Life history theory in psychology (LHT-P) posits that early harshness and unpredictability levels influence one’s life history strategy (LHS), a suit of traits and developmental milestones that can include reproduction, risky behaviour, aggression and violence, attachment, and more. Perhaps the strongest association studied is that proposed by psychosocial acceleration theory (PAT), which predicts that higher levels of harshness and unpredictability (especially father absence) is associated with earlier puberty and sexual debut, especially in females. However, recent criticisms have questioned whether LHS differs across humans, whether PAT’s assumptions are exaggerated, and whether any observed association is causal. Surprisingly, country-wide populational studies testing LHT-P and PAT’s assumptions, especially studies using longitudinal designs, are lacking. This dissertation used a mix of exploratory and confirmatory analytical approaches to determine if harshness and unpredictability early in life predict earlier and frequent reproduction using publicly available governmental data from the Brazilian Census, the American Community Survey, the Canadian Census and other Statistics Canada sources. We separated predictors and outcomes by 10 to 15 years across the different studies and tested models using cross-sectional and inverted timeline (i.e., harshness and unpredictability “predicting” earlier reproduction) methods. We used five geographic levels to assess predictions: Brazilian municipalities, US counties, and Canadian dissemination areas, census divisions, and provinces and territories. We also tested whether the proportion of visible minorities (i.e., Brazilian Black population, American Black and Hispanic or Latino population, and Canadian visible minorities and Indigenous population) are significant predictors of earlier and frequent reproduction. Results from Chapter 3 suggest that the proportion of the population that lacks resources, has bigger family sizes, and are young married mothers in Brazil predicted the proportion of young mothers and percentage of children 10 years later. We observed a similar finding in US counties data with a 14-year separation between predictors and outcomes. The percentage of Blacks in Brazil was not a significant predictor, but percentage of Blacks and of Hispanic or Latino in US populations was a negative and significant predictor of frequent reproduction. Chapter 4 showed that census divisions data yielded better results than dissemination areas, which indicates that geographical and populational stability results in a better model performance than larger sample sizes. The prevalence of children in low-income families and the percentage of children predict family size of one-parent families and more frequent reproduction 15 years later. However, contrary to LHT-P assumptions, higher unemployment and higher rents relative to income were predictive of smaller family size of one-parent families and less frequent reproduction. The proportion of Indigenous people was also predictive family size of one-parent families. The longitudinal model performed better than the model with reversed timeline. Results from Chapter 5 show that the interaction between the proportion of Indigenous people and the cost of living to income ratio was also predictive of earlier and more frequent reproduction in Canadian provinces and territories 15 years later, but the main effect of the proportion of Indigenous people was negatively associated with earlier and frequent reproduction. Overall, the analyses of population data support some assumptions of LHT-P and PAT literature, and suggest that visible minorities, especially in US and Canada, probably encounter sources of harshness and unpredictability that are generally not captured in LHT-P literature. Therefore, future studies could explore new measures of harshness and unpredictability to these different sources of environmental harshness and unpredictability to fully characterize environments experienced by visible minorities.
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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 | 0.016 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".