The Multilingual Turn in a Tutor Education Course: Using Threshold Concepts and Reflective Portfolios
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".