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A Pedagogical Venturing into the Three Sisters' Garden: Lessons of Attunement and Reciprocity in Education

2020· article· en· W4412368783 on OpenAlexaffvenue
Tim Skuce, Sharon Pelech

Bibliographic record

VenueJournal of Applied Hermeneutics · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsUniversity of LethbridgeBrandon University
Fundersnot available
KeywordsAttunementReciprocity (cultural anthropology)SociologyPedagogyPsychologyAestheticsSocial psychologyArtMedicine

Abstract

fetched live from OpenAlex

This paper explores the connections the authors make between their experiences in the classroom and the powerful work of Robin Wall Kimmerer (2013) in Braiding Sweetgrass: Indigenous Wisdom, Scientific Knowledge, and the Teaching of Plants; specifically, her chapter entitled “The Three Sisters.” Through Kimmerer’s work, we interpret our own experiences within the classroom. We also draw upon Hans-Georg Gadamer’s philosophical hermeneutics in general and his notion of Erfahrung in particular. We were inspired by the author’s insights into how she happened upon a “new teacher” for her students. As a result, this paper explores her work, as it provides an image of what it is to be present among students while honouring the topic’s own being and becoming--staying open to its future possibilities not yet known. The pedagogical turn away from predetermined outcomes to reciprocity, interconnectedness, and relationships is examined in how they help us understand teaching and learning.

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)0.007
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0110.027
Scholarly communication0.0060.009
Open science0.0010.009
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.272
GPT teacher head0.431
Teacher spread0.158 · 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 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

Citations0
Published2020
Admission routes2
Has abstractyes

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