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Record W4200596229 · doi:10.37119/ojs2021.v27i1.497

“Gratitude to Old Teachers”: Leaning into Learning Legacies

2021· article· en· W4200596229 on OpenAlexaffvenueabout
Maya T. Borhani

Bibliographic record

Venuein education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGratitudeMentorshipPoetryCurriculumFriendshipPsychologySociologyPedagogyPassionLiteratureAestheticsArtVisual artsSocial psychology

Abstract

fetched live from OpenAlex

Amongst a group of poet-scholar friends, all of us students of the American poet Robert Bly, often speak of our “gratitude to old teachers,” the title from one of Bly’s (1999) poems. We cherish a meditative awareness of deeply rooted presences holding us up, buoying us as we stride across “Water that once could take no human weight” that now “holds up our feet / And goes on ahead of us ….” What is this mystery? Through the love and support of “old teachers,” we are held, led, and supported, into an unknown future that, without their guidance, we might never have reached. Many of Bly’s students (myself included) refer to how meeting him “changed” or even “saved” their lives. Similarly, I could say this of meeting and studying with Canadian curriculum scholar and poet Carl Leggo. Practicing gratitude to old teachers fosters vital pedagogic engagement and personal connection in a world often fraught with isolation and despair. Reflecting on how these poetic influences have inspired and guided my own personal and professional life, this essay ruminates on grateful legacies within literary and curriculum studies, and beyond.
 Keywords: gratitude, curriculum studies, mentorship, poetry, poetic inquiry

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.014
GPT teacher head0.334
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 designQualitative
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
Published2021
Admission routes3
Has abstractyes

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