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Record W2146149879 · doi:10.7202/1072427ar

Normative Analysis and Moral Education: How May We Judge?

2020· article· en· W2146149879 on OpenAlexafffundvenue
David P. Burns

Bibliographic record

VenuePaideusis · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsUniversity of Alberta
FundersUniversity of British ColumbiaSimon Fraser UniversityUniversity of Alberta
KeywordsNormativeValue (mathematics)Context (archaeology)Philosophy of educationField (mathematics)Engineering ethicsEpistemologySociologyMoral educationPedagogyHigher educationPolitical sciencePhilosophyLawComputer scienceEngineering

Abstract

fetched live from OpenAlex

The viability of philosophy of education as a distinct and valued field of inquiry in educational research is under significant threat. While the debate over the proper role and value of philosophy of education continues, courses and faculty positions in philosophy of education become increasingly rare. I advance the view that this situation requires philosophers of education find new ways to bring their work to practicing educators. I propose a particular kind of normative analysis, within the context of moral education, as one way to bring valuable philosophic work to the daily practice of teaching. It is argued that the use of normative criteria, comprised of certain key characteristics for moral education, can serve not only as valuable analytic tools but may also draw practicing educators into conversations that generally take place between philosophers of education in the academy.

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)0.039
metaresearch head score (Gemma)0.087
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 score0.039
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.087
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.003
Science and technology studies0.0080.100
Scholarly communication0.0220.041
Open science0.0040.007
Research integrity0.0110.016
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.051
GPT teacher head0.334
Teacher spread0.283 · 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 routes3
Has abstractyes

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