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Record W2050822995 · doi:10.1177/1532708610374813

Subverting the Ivory Tower: Teaching and Learning Through Critical Dialogues

2010· article· en· W2050822995 on OpenAlexaffabout
Elizabeth J. Meyer, Veronika Lesiuk

Bibliographic record

VenueCulture Studies &#x2194 Critical Methodologies · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsIvory towerTowerMathematics educationPedagogyExperiential learningSociologyVisual artsPsychologyArtHistoryArchaeologyPhilosophyLinguistics

Abstract

fetched live from OpenAlex

Joe Kincheloe was our teacher, our mentor, and our friend. In our experiences in higher education, we had learned that it was virtually impossible to have these three different kinds of relationships with one person; particularly with an established, respected scholar who was as prolific as he was. He wasn’t arrogant or inaccessible or a diva or blinded by the hubris that can come with being deemed an “expert in the field.” He was Joe, the Vols fan and blues musician from Tennessee who proudly told anyone who would listen about his honorary membership in the Lesbian Avengers. When we first met him, he was also Dr. Joe L. Kincheloe, the Canada Research Chair in critical pedagogy at McGill University and renowned scholar and author or editor of more than 40 books. However, the mere presence of Joe and his work in cultural studies, social theory, and critical pedagogy existing in the Ivory Towers of top-tier research universities was an act of subversion. He preferred T-shirts and jeans to jackets and ties. He befriended the custodial staff before getting to know the department chairs, and he never made it to a meeting on time because he was busy listening to someone. Even more than his presence, his teaching subverted the expectations and the unwritten rules of these elite institutions.

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)0.008
metaresearch head score (Gemma)0.356
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.827
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.356
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.007
Scholarly communication0.0000.000
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 head0.419
GPT teacher head0.540
Teacher spread0.121 · 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

Citations3
Published2010
Admission routes2
Has abstractyes

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