What Kind of Citizen is Philosophy for Children Educating? What Kind of Citizen Should it be Educating?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.036 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".