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Record W7132957818

Expressivist Pedagogy: Re-conceptualizing Writing through Narratives of Self in a Postsecondary Writing Classroom

2023· dissertation· W7132957818 on OpenAlexaff
Sunnya Khan

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldSocial Sciences
TopicPosthumanist Ethics and Activism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRhetorical questionNarrativeReflexivityChoseLife writingAcademic writingAction (physics)Professional writingRhetorical modes
DOInot available

Abstract

fetched live from OpenAlex

What magic transpires when we give our academic experiences a personal tongue? This research sought to unravel the alternative knowledges spawned through heretical texts when students begin to trust their own experiences to lead them to meaning. To understand this, I taught and observed an Expressive Writing classroom at an Arts College in Lahore, Pakistan. I specifically wanted to explore: 1) How do students re-conceptualize writing after taking a course built on expressivist principles? 2) How does student writing change over the course using a rhetorical analysis as a guiding framework? and 3) How do student writers respond to an expressivist pedagogy? Using a narrative action oriented case study design, I collected data through semi-structured interviews with two participants, student writings, classroom observations, reflexive diary and exit tickets gathered from all students enrolled in the course. The study showed that students re-conceptualized writing as dialectical, relational, communal, situated, liberating, intuitive and transformative. Students took control of their narratives, wrote for a real audience in conversations with one another, started to understand life through the stories they chose to tell, developed their own voice/s in the midst of others, and saw their own lives as sources of meaning. Knowledge was no longer conceptualized as some elusive entity “over there,” external from them. Overall, the writing workshop model of the course was a palatable experience for the majority of students while some found that writing in discourse communities felt disconcerting. Student writing displayed a preliminary understanding of rhetorical conventions (discrepancy between defining good writing and writing good writing) and contained resonant passages (writings with added presence) of gendered rhetoric typifying Shock Expressive Writing—deeply visceral writing which shocks the reader into revisionism, meta-transcendence and/or alternative thinking through authentic expression. Based on the findings of this action research, implications include recommendations for teachers who want to teach expressive writing which centers indigeneity, decolonizes writing pedagogy, and is rooted in holistic education and a pedagogy of love (bridging the gap between the academic and the heart). Concomitantly, this study also makes a case for the language of love—a novelty—to permeate academia.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.018
metaresearch head score (Gemma)0.016
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 score0.018
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0070.032
Scholarly communication0.0170.016
Open science0.0030.011
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0020.000

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 head0.067
GPT teacher head0.439
Teacher spread0.373 · 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

Labeled directly by 2 models reading the full record.

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
Published2023
Admission routes1
Has abstractyes

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