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Record W3137182845 · doi:10.1080/17449642.2021.1896634

Be the village: exploring the ethics of having children

2021· article· en· W3137182845 on OpenAlexaff
David Chang

Bibliographic record

VenueEthics and Education · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPopulationSustainabilityPopulation controlEnvironmental ethicsSustainable developmentEcological crisisEnvironmental educationSubject (documents)Population growthSociologyEcologyPolitical scienceLawFamily planning

Abstract

fetched live from OpenAlex

The rapid increase in human population is one of the underlying factors driving the ecological crisis. Despite efforts on the part of educators to raise awareness of environmental issues, the ecological impact of a burgeoning population – and the ethical implications of having children – remains an unbroachable topic. Nevertheless, the increase in human numbers is central to questions of sustainability: How can a species expect to survive in a finite terrestrial environment without limits to its population?Since most of the world’s ecological impact can be traced to capitalist-industrial- consumer societies in over-developed nations, the middle and upper classes in rich countries must weigh the ecological consequences of their family-planning decisions. In this paper, I argue that educational programs that are concerned with environmental ethics should have students examine the assumptions and implications of having children. I consider the risks associated with this proposal and respond to a series of possible objections. This paper does not advocate coercive measures for population control, but rather enjoins a pedagogical responsibility to view having children as an act with ecological consequences, an act that must be subject to careful examination.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.052
GPT teacher head0.321
Teacher spread0.269 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2021
Admission routes1
Has abstractyes

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