MétaCan
Menu
Back to cohort
Record W7132991412

Managing Tensions in Equitable Disciplinary Teaching

2023· dissertation· W7132991412 on OpenAlexaff
Monika Zenobia Moore

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsCanadian Association for the Study of Adult Education
Fundersnot available
KeywordsDisciplineFace (sociological concept)Higher educationReflection (computer programming)Curriculum
DOInot available

Abstract

fetched live from OpenAlex

As representatives of a discipline and gatekeepers to disciplinary communities, faculty have a powerful role in defining what disciplines are and who has access. In exposing students to disciplinary conventions, instructors may present disciplinary norms as inflexible, which may limit access or make disciplines feel unwelcoming. Students may be faced with reconciling their culture’s ways of knowing with disciplinary ways of knowing, and instructors may face conflicts between preparing students for disciplinary expectations and providing an equitable classroom open to non-dominant ways of knowing and communicating. This multiple case study investigates how faculty think about disciplinary norms and equitable pedagogies, whether tensions arise in their disciplinary teaching, and how they manage those tensions. Findings indicate that tensions created opportunities for reflection and led to changes in teaching indicative of double-loop learning. Departmental tensions suggest a paradigm shift in Earth Science teaching pedagogy, and institutional tensions suggest that the university is undergoing organizational learning. This study also indicates that tensions occurred at multiple levels (individual, departmental, discipline, and institution/university) which has implications for challenges in making learning environments more equitable. Existing and potential approaches for addressing these challenges are discussed.

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)0.014
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.002

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 head0.187
GPT teacher head0.538
Teacher spread0.351 · 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 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

Explore more

Same venueTSpaceSame topicEvaluation of Teaching PracticesFrench-language works237,207