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Record W3035690107 · doi:10.7202/1070276ar

What Kind of Citizen is Philosophy for Children Educating? What Kind of Citizen Should it be Educating?

2020· article· en· W3035690107 on OpenAlexaffvenue
Olivier Michaud

Bibliographic record

VenuePhilosophical Inquiry in Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsPhilosophy for ChildrenVisionDemocracyPedagogyPhilosophy of educationCitizenshipSociologyEpistemologyPolitical scienceHigher educationPoliticsLawPhilosophy

Abstract

fetched live from OpenAlex

Philosophy for Children (henceforth P4C) is a program and a pedagogy for teaching philosophy in k-12 school that was first developed by Matthew Lipman and Ann Margaret Sharp. The P4C approach is generally presented as a valuable form of education for democratic citizenship. This relationship is so obvious that it often remains underdeveloped: P4C is constructed with the goal of developing children to be critical thinkers and to know how to dialogue with others, which are also hallmarks of what is wanted in the citizens of our democracies. The objective of this article is to explore and deepen this connection by analyzing how it has been developed in the literature of the P4C movement. What emerges from this study is that there are differences of opinions as to why P4C is an appropriate kind of education for democracy. From the texts analyzed, three pedagogies stood out in that regard: Deweyan pedagogy, critical pedagogy, and pedagogy of interruption. I analyze the different visions of P4C as a democratic education in each of these, present the different criticisms they offer to P4C in that regard, and propose how P4C may answer these criticisms. I conclude with the importance of practitioners being aware of the different perspectives that encompass P4C concerning its role for education for democratic citizenship.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.593
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.164
GPT teacher head0.418
Teacher spread0.254 · 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

Citations15
Published2020
Admission routes2
Has abstractyes

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