The Problem of Overpopulation: Proenvironmental Concerns and Behavior Predict Reproductive Attitudes
Bibliographic record
Abstract
Human overpopulation continues to be a pressing problem for the health and viability of the environment, which impacts the survival and well-being of human populations. Limiting the number of offspring one produces or deciding to remain child-free may be viewed as a proenvironmental behavior (PEB) that can significantly reduce one's carbon footprint. Nonetheless, few researchers have examined the relations between environmental concerns, reported PEB, and reproductive attitudes. The goal of the current study was to examine the above relations in a sample of 200 Canadian undergraduates. Environmental concern as part of an ecologically conscious worldview (the New Ecological Paradigm) was found to negatively predict pro-reproductive attitudes. In contrast, more self-oriented (egoistic) and human-centric (altruistic) environmental concerns positively predicted pro-reproductive attitudes. Additionally, self-reported PEB was found to negatively predict pro-reproductive attitudes. All of the above relations were found to be statistically significant while controlling for the influence of age, sex, and religious status. These findings add to a limited empirical literature on environmental concerns, PEB, and attitudes toward reproducing, which can help inform discussion regarding the environmental issues associated with human overpopulation and potential ways to mitigate these dilemmas.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".