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Transformation and Education: The Voice of the Learner in Peters' Concept of Teaching

2009· article· en· W1578494092 on OpenAlexaff
Andrea R. English

Bibliographic record

VenueJournal of Philosophy of Education · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Philosophies and Pedagogies
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsContext (archaeology)Transformational leadershipPhilosophy of educationTeaching methodPedagogyEpistemologySociologyMathematics educationPsychologyHigher educationPhilosophySocial psychology

Abstract

fetched live from OpenAlex

On several occasions in his work, R. S. Peters identifies a difficulty inherent in teaching that underscores the complexity of this relationship: the teacher has the task of passing on knowledge while at the same time allowing knowledge that is passed on to be criticised and revised by the learner. This inquiry asks: first, how does Peters envisage these two tasks coming together in teaching, and, second, does he go far enough in developing what it means for the teacher to recognise the difference and otherness of the learner? At the heart of the matter in answering these questions is how to conceive of the connection between learning and the transformation of the individual. Before turning to Peters, this inquiry begins by discussing connections between transformation and learning in classical and contemporary philosophical discourse. In this context, notions of discontinuity and interruption are shown to be central to understanding transformational learning processes. Peters' thought is then located within this discourse and taken up in three central ways: (1) by analysing his notion of teaching (2) by examining the ideas of learning and transformation embedded in his concept of teaching, and (3) by inquiring into how these issues relate to his idea of philosophy of education as a central part of teacher education.

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)0.000
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: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.188

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.034
GPT teacher head0.355
Teacher spread0.320 · 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

Citations26
Published2009
Admission routes1
Has abstractyes

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