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Record W4412924725 · doi:10.46586/jdph.2025.12139

Educating For Self-Legislation Within An Emotional Landscape

2025· article· en· W4412924725 on OpenAlexaff
Susan Gardner

Bibliographic record

VenueJournal of Didactics of Philosophy · 2025
Typearticle
Languageen
FieldPsychology
TopicPhenomenology and Existential Philosophy
Canadian institutionsCapilano University
Fundersnot available
KeywordsLegislationPsychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

According to Kant, self-direction requires that one’s reasoning power reigns supreme, a view that has, for decades, supported “stand alone” critical thinking classes. In contrast, some argue for the need to reason together across difference hence the growing popularity of Communities of Philosophical Inquiry. It will be argued here that this focus is still too narrow. If “togetherness” is necessary for excellent reasoning, since, clearly, we are emotional beings as well as reasoning beings, such contexts requires that we take responsibility for managing the emotional environment in which that reasoning takes place. Such responsibility is sorely lacking in contemporary culture wars that condone self-righteous insult-flinging, define attempts to self-shield as evidence of deserved guilt, and that perpetrate widespread emotional fragility in the name of safety. Hence, it will be argued that it is imperative that we stop reproducing the error attributed to Descartes of assuming a quasi-disconnect between mind and body by embracing educational strategies that focus entirely on perfecting reason. The goal, rather, must be to educate so that we all take responsibility for creating “we contexts” by ensuring that the emotional landscape to which we inevitably contribute is amenable to the possibility of reasonable interchange and self-direction.

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)0.008
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 score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0050.021
Scholarly communication0.0070.009
Open science0.0010.008
Research integrity0.0030.010
Insufficient payload (model declined to judge)0.0050.002

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 head0.038
GPT teacher head0.349
Teacher spread0.311 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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