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Record W3089272564 · doi:10.31468/cjsdwr.807

The Multilingual Turn in a Tutor Education Course: Using Threshold Concepts and Reflective Portfolios

2020· article· en· W3089272564 on OpenAlexvenueno aff
Hidy Basta

Bibliographic record

VenueDiscourse and Writing/Rédactologie · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWriting centerTUTORReflection (computer programming)PortfolioIdeologyDiversity (politics)PedagogySociologyReflective practiceReflective writingHigher educationPsychologyMathematics educationEngineering ethicsComputer sciencePolitical scienceBusinessEngineering

Abstract

fetched live from OpenAlex

In this article, I reflect on efforts to revise the instruction and evaluation of an undergraduate writing consultant education course. The revisions are motivated by the desire to adopt practices that reflect the writing center’s commitment to social justice for multilingual/translingual students and by a commitment to provide an effective, flexible, and brave environment for writing consultants to continue their professional development. I argue that grounding understanding of multilingual writers in concepts that explicitly explore linguistic diversity and standardized 1 English ideologies as threshold concepts is essential to reconceptualize writing center practices. I also argue for the necessity of adopting a flexible system for reflection, engagement, and evaluation to support writing consultants’ learning and practice. I share prompts used in the course and some of the responses they generated. The responses suggest that although combining threshold concepts with a portfolio system is successful in supporting inclusive practices, there remains a need to expand more inclusive practices across the university.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.615
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.125
GPT teacher head0.441
Teacher spread0.316 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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