MétaCan
Menu
Back to cohort
Record W2145262183 · doi:10.14434/josotl.v15i4.13339

I’ve Got You Covered: Adventures in Social Justice-Informed Co-Teaching

2015· article· en· W2145262183 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the Scholarship of Teaching and Learning · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPedagogySociologyNarrativeSocial justicePerspective (graphical)Context (archaeology)Teaching methodEthnographyEconomic JusticeNarrative inquiryAdventurePsychologySocial sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

What is social justice-informed co-teaching? Why is it important? How can it enrich social justice pedagogy? While the answers to these questions may vary depending on context and perspective, they are nevertheless useful to address. Each of these questions will be discussed in this research paper. This auto-ethnographic narrative inquiry adds to the literature on social justice-informed co-teaching in an innovative way. It critically examines the purposeful endeavor of two professors who used social justice thinking to guide their co-teaching practice, and simultaneously used co-teaching to enrich their social justice pedagogy. At once, this paper is a lived experience, a story, and a research study. In deconstructing two narratives, the authors articulate specific ways in which co-teaching, as a practice, presents unique opportunities for social justice learning. Implications for research and practice in teacher education programs, teaching practices and field- experiences, and co-teachers themselves are shared in the closing segment of the paper.

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.

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)0.022
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.005
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 head0.050
GPT teacher head0.391
Teacher spread0.342 · 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