MétaCan
Menu
Back to cohort
Record W2105387095 · doi:10.7202/013343ar

Critical Literacy and the Un/Doing of Academic Discourse

2006· article· en· W2105387095 on OpenAlexaffvenue
Catherine G. Taylor

Bibliographic record

VenueEthnologies · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsCritical literacyPrivilege (computing)SociologyEmpowermentPedagogyInstitutionAcademic writingLiteracyFace (sociological concept)Prejudice (legal term)Critical race theoryCritical theoryPower (physics)Critical pedagogyMathematics educationPsychologyEpistemologySocial sciencePolitical scienceSocial psychologyGender studiesRace (biology)

Abstract

fetched live from OpenAlex

This article describes a “critical literacy” approach to teaching an introductory course in academic writing to a group of students socially marginalized by poverty and prejudice. In this approach, the author asks students to identify research areas of personal interest to themselves by drawing on their own lives to generate meaningful topics, to engage in critical dialogue about their work, and to write in a scholarly style without becoming alienated from their work. She also welcomes students’ critical observations on their own experience of dominant culture and invites them to scrutinize the academic world they are entering as an institution that participates fully in systems of privilege and power. In her experience, much of the work done by students engaged in a such an approach demonstrates a higher degree of the critical insight and serious-minded knowledge-building valued in academia than is normally seen in introductory writing courses. She articulates a rationale for using this empowerment-based method of teaching academic writing in the face of calls for a return to traditional methods, and provide an analysis centred in Critical Literacy theory to account for its successes and challenges.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.635
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.087
GPT teacher head0.456
Teacher spread0.370 · 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
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueEthnologiesSame topicTeacher Education and Leadership StudiesFrench-language works237,207