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Record W4280589025 · doi:10.15173/ijsap.v6i1.4892

“Radical TAs”: Co-creating liberatory classrooms with undergraduate students

2022· article· en· W4280589025 on OpenAlexaffvenue
Mattie Schaefer, Tenaja Henson, Rehshetta Wells, Sarena Ezell, Judia Holton, Donzahniya Pitre, Krista Craven

Bibliographic record

VenueInternational Journal for Students as Partners · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsCarleton University
FundersUniversity of Vermont
KeywordsTransformative learningLiberal arts educationCurriculumPedagogyClass (philosophy)Student engagementCitizen journalismSociologyMathematics educationPoliticsCo-teachingPsychologyHigher educationPolitical scienceComputer science

Abstract

fetched live from OpenAlex

In this paper, we suggest that when undergraduate students are engaged as full teaching partners with professors in the college classroom, more liberatory and transformative educational spaces can be created. This paper is based on findings from a qualitative participatory study led by a team of six undergraduate students and one professor who engaged in a series of collaborative teaching endeavors (known as the Radical Teaching Assistant Project) at a small liberal arts college in the southern United States. Our findings suggest that positioning undergraduate students as co-teachers in college classrooms (a) fosters deeper student engagement through relatability, (b) creates more accessible and generative learning environments, (c) subverts knowledge hierarchies in the academy, (d) challenges dominant discourses and norms in the classroom, and (e) provides a space to engage in prefigurative politics. We also discuss some key challenges that arise through this model of collaborative teaching. Our findings suggest that students have much to offer college classrooms when they are central actors in designing course curricula and facilitating class sessions for their peers.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.869
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.522
Teacher spread0.492 · 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.

Study designNot applicable
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

Citations1
Published2022
Admission routes2
Has abstractyes

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