Bibliographic record
Abstract
This article discusses using poetic transcription (Glesne, 1997) as a tool for examining trust, including what trust looks and feels like from the “lived experiences” (Richardson, 1992) of university educators. We first explore the rationale for using poetic transcription in this study, discussing how and why poetry may be used as a profound narrative tool (Wells, 2004). This is followed by an overview of the methodology of poetic re-presentation (after Butler-Kisber, 2010; Glesne, 1997; Richardson, 2003). Next, we present selected poems from three different educators (a retired teacher, graduate student teacher, and teacher/administrator). We discuss critical insights and perspectives on trust, including ways that poems can evoke emotion, build empathy, and elicit deeper understandings of the topic at hand (Cannon Poindexter, 2002; Clunis D’Andrea, 2013; Eisner, 1997; Faulkner, 2007; Furman, Lietz, & Langer, 2006). We conclude by discussing some of reasons why poetry is ideally suited for broadening the horizons of educational research. Dans cet article, nous traitons de l’emploi de la transcription poétique (Glesne, 1997) en tant qu’outil pour examiner la confiance, y compris la manière dont la confiance paraît et se ressent à partir d’« expériences vécues » (Richardson, 1992) par des éducateurs universitaires. Tout d’abord, nous explorons pourquoi nous avons utilisé la transcription poétique dans cette étude, nous discutons comment et pourquoi la poésie peut être utilisée en tant qu’outil narratif profond (Wells, 2004) pour découvrir le coeur de la confiance. Ensuite, nous présentons un aperçu de la méthodologie de la représentation poétique (après Butler-Kisber, 2010; Glesne, 1997; Richardson, 2003). Puis nous présentons quelques poèmes choisis de trois éducateurs différents (un professeur à la retraite, un étudiant diplômé enseignant et un enseignant/administrateur). Nous discutons les idées critiques et les perspectives sur la confiance, y compris les diverses manières dont les poèmes peuvent évoquer l’émotion, faire éprouver l’empathie et susciter une compréhension plus profonde sur le sujet à l’étude (Cannon Poindexter, 2002; Clunis D’Andrea, 2013; Eisner, 1997; Faulkner, 2007; Furman, Lietz et Langer, 2006). En conclusion, nous discutons quelques-unes des raisons pour lesquelles la poésie se prête de façon idéale à la recherche en éducation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".