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Record W2749192079 · doi:10.1093/pq/pqx045

The Human Predicament: A Candid Guide to Life's Biggest Questions

2017· article· en· W2749192079 on OpenAlexaff
David Matheson

Bibliographic record

VenueThe Philosophical Quarterly · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicPhilosophical Ethics and Theory
Canadian institutionsCarleton University
Fundersnot available
KeywordsArgument (complex analysis)PessimismPleasureEpistemologyFocus (optics)PsychologyQuality (philosophy)AestheticsPhilosophySocial psychologySociologyMedicine

Abstract

fetched live from OpenAlex

In his earlier contribution to the philosophy of life, Better Never To Have Been (OUP, 2006) Benatar presented two core arguments. According to his ‘asymmetry’ argument, whereas to begin living (or ‘to come into existence’) is always partly harmful and partly beneficial, not to begin living is never even partly harmful; and if this is so, then it is always better not to begin living than to begin living. According to his quality of life argument, the quality of every (human) life is poor. Once we discount quality of life judgments that are unduly influenced by our distorting optimistic biases, the argument went, we can see clearly that the amount, distribution, and axiological weight of every life's positive qualities (e.g., pleasure, desire-satisfaction, the goods on which ‘objective list theories’ focus) are swamped by the amount, distribution, and weight of its negative qualities (e.g., pain, desire-frustration, and objective list bads). The asymmetry argument plays no important role in Benatar's latest contribution. But the quality of life argument does: with minor modifications, it serves as one main element of the overall case that Benatar makes in The Human Predicament for his broadest pessimistic contention yet. Simply put, this contention is that, judged by any relevant measure, all lives are bad in the sense that they are regrettable or unfortunate—instances of a ‘tragic predicament’.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0100.003
Scholarly communication0.0020.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.046
GPT teacher head0.314
Teacher spread0.268 · 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.

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

Citations24
Published2017
Admission routes1
Has abstractyes

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