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Record W2502945249 · doi:10.1017/cbo9780511783012.004

Happiness and the moral life

2011· book-chapter· en· W2502945249 on OpenAlexaff
Sonia Sikka

Bibliographic record

VenueCambridge University Press eBooks · 2011
Typebook-chapter
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHappinessEnvironmental ethicsPsychologySociologySocial psychologyPhilosophy

Abstract

fetched live from OpenAlex

At one time, during the 1760s, Herder had been a student of Kant's, and had greatly admired the views communicated in his lectures of that period. Kant's highly unsympathetic review of the second part of Herder's Ideas for a Philosophy of the History of Mankind , published in 1785, however, shows how profound the philosophical differences between these two had become by this point. The nature of happiness, and its place within the “destiny” or “vocation” ( Bestimmung ) of the human race, forms a central area of dispute emerging from the review. Kant is responding, in particular, to a section of the Ideas entitled: “The happiness ( Glückseligkeit ) of human beings is everywhere an individual good; consequently, it is everywhere climatic and organic, a child of practice, tradition, and custom” ( Ideas , 327). Although he is not mentioned by name, this section clearly contains critical rejoinders, often quite harsh in tone, to aspects of Kant's practical philosophy and philosophy of history, as Herder understands them. Against the idea that happiness requires extrinsic justification, for example, Herder insists that “every living creature takes delight in its life; it does not brood and ask, why is it there? Its existence is to it an end and its end is existence” ( Ideas , 330).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.008
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.023
Scholarly communication0.0080.006
Open science0.0000.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.050
GPT teacher head0.232
Teacher spread0.182 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2011
Admission routes1
Has abstractyes

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