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Record W2136053690 · doi:10.1080/03057240.2011.568101

Bearing and transcending suffering with nature and the world: a humanistic account

2011· article· en· W2136053690 on OpenAlexaff
Rosa Hong Chen

Bibliographic record

VenueJournal of Moral Education · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicCritical and Liberation Pedagogy
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsHumanismEpistemologyHumanistic psychologyPsychologyHumanistic educationBearing (navigation)PhilosophySocial psychologySociologyPsychoanalysisTheology

Abstract

fetched live from OpenAlex

To conceptualise moral education as ‘living and learning to bear suffering’ offers a humanistic vision for choices people make in the face of drastic threats to their existence. This essay proposes that bearing and transcending suffering—part of the human narrative—helps human beings to realise their ethical potential. Grounded in Eastern and Western metaphysics and ethics, I assess the human condition brought about by the 2008 earthquake disaster in China—in an attempt to come to terms with fundamental philosophical questions of existence and human values. While raising questions about how human beings are intrinsically interrelated to Nature and the world, this account is linked by a thread of humanism encompassing three important values (caring, responsibility and free spirit). I conclude by suggesting that educating young people for the wisdom of suffering is to cultivate a humanistic morality. Ethical implications for ecology and moral education are considered.

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)0.004
metaresearch head score (Gemma)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.056
Scholarly communication0.0050.007
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.347
Teacher spread0.307 · 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
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

Citations7
Published2011
Admission routes1
Has abstractyes

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