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Record W3034442556 · doi:10.46707/jps.v7i.106

Does philosophy kill culture?

2020· article· en· W3034442556 on OpenAlexaff
Jason Chen, Susan T. Gardner

Bibliographic record

VenueJournal of Philosophy in Schools · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsCapilano University
Fundersnot available
KeywordsHarmony (color)NothingFace (sociological concept)SociologyVirtueAestheticsEpistemologyPsychologyPhilosophySocial scienceArt

Abstract

fetched live from OpenAlex

Given that one of the major goals of the practice of Philosophy for Children (P4C) is the development of critical thinking skills (Sharp 1987/2018, pp. 4 6), an urgent question that emerged for one of the authors, who is of Chinese Heritage and a novice practitioner at a P4C summer camp (thinkingplayground.org), was whether this emphasis on critical thinking might make this practice incompatible with the fabric of Chinese culture. Filial piety (孝), which requires respect for one’s parents, elders, and ancestors is considered an important virtue in Asian culture, as is the preservation of harmony. But if one of the goals of P4C is to teach youngsters to courageously pursue reasoned dialogue, does this not set-up young Asians for serious conflict when they come face-to-face with positions that are articulated by elders, but which are ones to which they are diametrically opposed; a racist grandmother, for instance, or an uncle who insists that those at the Tiananmen Square uprising were nothing but hooligan’s. It is this question that we will explore in this presentation. In the process, we will come to the conclusion that, when positions seem irreconcilable, rather than continuing to pursue rigorous critical interchange that may do little other than escalate insult, the facilitator, rather, ought to move toward creating a deeper understanding of each position juxtaposed against its opposing view (a process that we refer to as ‘collaborative caring’), so as to produce side-by-side understanding, knowing that communal bonds have been maintained and, hence, that the opportunity for genuine reasoned collaborative inquiry on other issues and at future times remains open.

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)0.010
metaresearch head score (Gemma)0.012
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 score0.015
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0120.078
Scholarly communication0.0150.024
Open science0.0020.010
Research integrity0.0050.013
Insufficient payload (model declined to judge)0.0110.002

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 head0.047
GPT teacher head0.337
Teacher spread0.290 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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