MétaCan
Menu
Back to cohort
Record W2905043009 · doi:10.4324/9781315208732-11

Showing that children can do philosophy

2017· book-chapter· en· W2905043009 on OpenAlexaboutno aff
Michel Sasseville

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive scienceComputer sciencePsychology

Abstract

fetched live from OpenAlex

Your question reminds me the comments I heard many times when I tried to bring P4C to Laval University 30 years ago. Many were saying: well, if it is philosophy it is not for children and if it is for children it can’t be philosophy. Twenty years later, this comment totally disappeared. What happened? I would say that the research of Vygotsky (1985) is now well known in the field of education. Piaget is no more the king in this discipline. Vygotsky’s insights, supported by many researches after his death, show clearly that children can work with abstract notions. Some of my colleagues have rethought their conception of philosophy and, more specifically, their conception of how philosophy can be taught. If, as Lipman and Sharp did (1980 , 1984 , 1988 , 1991, 1992 ),we redesign the teaching of philosophy in such a way that this discipline can be interesting and useful for children, then the possibility of doing it with children is evident. We have seen a profound reform in education in Québec over the past 30 years and now, in primary school, we talk about competencies and transversal competencies and making critical judgments. More than that, the Ministry of Education of Québec says that the classroom should be transformed into a community of learners. In this context, Philosophy for Children (P4C) is more than welcome because doing philosophy with children means inviting them to become critical thinkers, not only that, but also that within a 90 community of inquiry. This is exactly what people in primary schools are looking for. And here we are with more than 40 years of experience showing how this could be done and the impacts of doing this on the performance of the child in other disciplines. It is no surprise that people are more and more interested in P4C. For nearly 30 years, we have trained thousands of teachers (by means of programs of formation, see https://philoenfant.org ) who have learned how to do philosophy with children. So, with their help, we have collected a series of discussions among children showing that they can do philosophy if they are assisted by a teacher who knows how to invite them to engage themselves in philosophical inquiry. For sure, we can talk and talk theoretically about the capacity or not of the child to do philosophy. But there is nothing better than a base of observation to talk about it. And this base shows clearly that children can do philosophy. In 2004, with the help of Laval University and Canal Savoir (an educative channel television in Québec), we have created a television series of 13 shows (30 minutes each) about Philosophy for Children. Called “ Des enfants philosophent ,” ( Sasseville, 2004 ) this television series shows that children (from 6 to 12 years old) can engage themselves in a philosophical inquiry. What we see in this series is children trying to define concepts like friendship, love, war, curiosity, difference, justice, freedom, and on and on in such a way that they give reasons, examples, counter-examples, formulate hypothesis, are looking for criteria. All these moves (and many more) are those we can observe when we look carefully at what philosophers are doing. If there is a difference, it is only a difference of degree, not of kind. Just like when we see children playing baseball (or hockey or football). Even if they are not professional, no one would say that they are not playing baseball.

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)0.008
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.030
Scholarly communication0.0100.014
Open science0.0010.010
Research integrity0.0040.012
Insufficient payload (model declined to judge)0.0220.005

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 head0.080
GPT teacher head0.325
Teacher spread0.245 · 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 designNot applicable
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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicEducation and Critical Thinking DevelopmentFrench-language works237,207