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Record W3157197944 · doi:10.24908/iqurcp.7542

5.  Attitudes on Investment in Descendants: Products of Evolution?

2017· article· en· W3157197944 on OpenAlexvenueno aff
Aaron Myran

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsReproductive successWelfareParental investmentPsychologyDemographyEconomicsBiologyPopulationSociologyPregnancyOffspring

Abstract

fetched live from OpenAlex

This study aims to examine intergenerational equity (concern for future generations) from a human evolutionary ecology perspective. The extent to which males and females differ in their concern about the welfare of future generations can be interpreted as a product of natural selection. We hypothesized that reproductive advantage is conferred upon males by discounting future generations and focusing on their immediate well being. Given that males potentially have many children, and do not invest the same resources in raising children, it is to their reproductive advantage to focus on the present and their own wellbeing, so that they can continue to reproduce. It is hypothesized that reproductive advantage would be conferred upon females who are concerned about future generations. Females potentially have fewer children and invest more in raising children. It is therefore more to their reproductive advantage to ensure that children survive to adulthood. Using a between‐subjects design online survey, emailed to individuals in the Queen's Community (Students, Staff, Faculty, and Alumni), we asked participants to indicate what proportion of available money ($10, 000 they received by chance) they would allocate to mitigating a hypothetical food crisis (collectivist option) versus to three “individualist” options. Participants were randomly assigned to hypothetical scenarios where the food crisis affected: the participant's generation, their children's generation, their grandchildren's generation, or their great‐ grandchildren's generation. In all four scenarios, we found that males invested significantly less in mitigating the food crisis than females. Additionally, we found that neither males nor females differed significantly between scenarios in the amount they invested in mitigating the food crisis.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.165
GPT teacher head0.440
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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