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Record W2521645120 · doi:10.36510/learnland.v4i1.367

The Poetics of Self-Study: Getting to the Heart of the Matter

2010· article· en· W2521645120 on OpenAlex
Margaret Dobson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueLEARNing Landscapes · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicHannah Arendt's Political Philosophy
Canadian institutionsMcGill University
Fundersnot available
KeywordsPoeticsPoetryReading (process)LiteratureAestheticsPsychologySociologyArtPhilosophyLinguistics

Abstract

fetched live from OpenAlex

This paper illustrates what poetic inquiry may contribute to teaching and learning. By using four samples of my own poetry, I demonstrate how the poetics of self-study can release the inner voice in ways that perhaps more conventional forms of research may not. Poetry removes the insulation between me and my experience to reveal what is "true" for me. Reading my own poetry as text has reinforced my educational convictions: It is who we are, not what we do (Arendt, 1974, p. 179) that will determine the success of our best classroom practices.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.693
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.284
Teacher spread0.274 · 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