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Record W2418365381 · doi:10.7202/1070458ar

Philosophy for Children Meets the Art of Living: A Holistic Approach to an Education for Life

2020· article· en· W2418365381 on OpenAlexvenueno aff
Laura D’Olimpio, Christoph Teschers

Bibliographic record

VenuePhilosophical Inquiry in Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsPrudencePhronesisEpistemologyEmbodied cognitionThe good lifePhilosophy for ChildrenParallelsSociologyIdeal (ethics)AestheticsPsychologyPhilosophyEngineering

Abstract

fetched live from OpenAlex

This article explores the meeting of two approaches towards philosophy and education: the philosophy for children (P4C) approach advocated by Lipman and others, and Schmid’s (2000) philosophical concept of Lebenskunst (the art of living). Schmid explores the concept of the beautiful or good life by asking what is necessary for each individual to be able to develop their own art of living and which aspects of life are significant when shaping a good and beautiful life. One aspect of Schmid’s theory is the practical application of philosophy through the notions of Bildung, (self-) refection, prudence and practical wisdom, as well as the requirement for each individual to take responsibility for actively shaping their life as an artwork. In this sense, each person is the artist responsible for living their own beautiful life. We argue that there are some useful parallels between Schmid’s concept of the art of living and P4C, such as the ideal of a holistic philosophy that is “lived”. The pragmatic approach of P4C focuses on the embodied learner who practices critical, caring and creative thinking. Both P4C and Schmid’s theory are reminiscent of the Aristotelian notion of practical wisdom (phronesis), which allows for an approach to an education for life that prepares (young) students to develop their own art of living. We also critically discuss tensions arising between these two concepts.

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)0.008
Version: codex-gemma-dda1882f352aValidation 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 score0.875
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.185
GPT teacher head0.414
Teacher spread0.229 · 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 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

Citations10
Published2020
Admission routes1
Has abstractyes

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