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Record W2971765347

Annotated Bibliography: Disability Studies and the place of diversity within the Scholarship of Teaching and Learning

2019· article· en· W2971765347 on OpenAlexaff
Joanna Rankin, Sarosh Sawani

Bibliographic record

VenueThe Journal for Research and Practice in College Teaching · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDiversity (politics)ScholarshipContext (archaeology)Scholarship of Teaching and LearningRelevance (law)Learning disabilityDisability studiesSocial justiceInclusion (mineral)Annotated bibliographySociologyField (mathematics)PedagogyPsychologyLibrary scienceSocial scienceTeaching methodPolitical scienceTeaching and learning centerComputer scienceGeographyGender studiesAnthropology
DOInot available

Abstract

fetched live from OpenAlex

This annotated bibliography has been shaped by the recognition of a relationship between the contemporary focus of diversity and social justice in SoTL and the role of teaching and learning in the field of Disability Studies. Considering the relevance of SoTL to the study of disability and to students and faculty with disabilities, this selection of literature identifies key topics that take up current teaching and learning theories and practices around diversity and social justices, as well as pointing to multiple intersections between these two areas. Specifically, readings included in this bibliography provide an overview of current literature in SoTL and Disability Studies and identify a recent heightened interest in the pursuit of social justice and diversity in post-secondary environments. Annotations review the bases of these two philosophies, as they relate to teaching and learning, explore the role of post-secondary students with disabilities in this context and provide a discussion of future directions

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)0.157
metaresearch head score (Gemma)0.035
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1570.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0070.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.003
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 head0.238
GPT teacher head0.515
Teacher spread0.277 · 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; both teacher heads agree on what is shown here.

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
Published2019
Admission routes1
Has abstractyes

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