Granny's legacy : did evolution select for grandmothers over an extended fertility window?
Bibliographic record
Abstract
Why are humans among the very few species in which females have an extended postreproductive lifespan? This question was first posed in 1957 by evolutionary biologist and original theorist of the Grandmother Hypothesis G.C. Williams, based on the widely accepted belief that menopause was uniquely human (Williams., 1957; Kim et al., 2018). Menopause occurs in human females well before the end of their anticipated life span and is classified as the permanent discontinuation of ovulation (Thouzeau & Raymond, 2017). Following the postulates of Darwin, any decrease in reproduction is counteractive to fitness, meaning that menopause essentially has no benefits to survival (Croft et al.,2015). The Grandmother Hypothesis asserts that grandmothers' benefits of caring for and aiding children and grandchildren counterbalance the price of lost reproduction (Cohen 2007). This hypothesis suggests that natural selection favours a prolonged post-reproductive lifespan if it allows individuals to enhance their fitness by aiding their offspring in successful reproduction. This research evaluates the utility of the Grandmother Hypothesis for understanding PRLS in Homo sapiens using historical (1790-1918) \nparish data from Nova Scotia. Using this data, I will identify whether fecundity and infant \nmortality rates follow the trends outlined by the Grandmother Hypothesis. Specifically, I will analyze whether there are shorter birth intervals in the mothers where their mother lives in close geographic proximity and if the child survives to reproductive age when their maternal grandmother is present. Although the results did not achieve statistical significance, the trends apparent in the data do follow the proposed trends of the Grandmother Hypothesis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".