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Record W4404004522 · doi:10.1002/hast.4941

Moral Nuances in Broad Policies

2024· article· en· W4404004522 on OpenAlexaboutno aff
Laura Haupt

Bibliographic record

VenueThe Hastings Center Report · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicWar, Ethics, and Justification
Canadian institutionsnot available
Fundersnot available
KeywordsBroad spectrumPolitical scienceEngineering ethicsEnvironmental ethicsLaw and economicsSociologyPsychologyEpistemologyPhilosophyEngineeringChemistry

Abstract

fetched live from OpenAlex

Abstract In the September‐October 2024 issue of the Hastings Center Report , two pieces examine attitudes toward and policy on medical aid in dying (MAID). An essay by Anna Elsner and colleagues analyzes terminology, including euphemistic language, used in Canada and other countries to refer to this practice. The authors recommend explicit public discussion of the values at stake in the use of this terminology. An article by Em Walsh concerns a subset of people who could become eligible for MAID under Canada's proposed expansion of eligibility for this assistance: people suffering from poverty‐induced depression. Cautioning that the expansion of PAD could exacerbate inequality, Walsh offers six recommendations for policy‐makers’ consideration. The issue's lead article, by Eric Juengst and colleagues, focuses on governance issues that could be raised by human genome editing research that aims to strengthen individuals’ resistance to disease beyond what is regarded as the human functional range. Juengst et al. identify and analyze three potential principles that could help policy‐makers navigate what can be a blurry line between goals of prevention and enhancement in human genome editing research.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.929
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.109
GPT teacher head0.301
Teacher spread0.192 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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