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Record W2472685502 · doi:10.64152/10125/66887

Using 'Close Reading' as a course theme in a multilingual disciplinary classroom

2015· article· en· W2472685502 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

VenueReading in a Foreign Language · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCourse (navigation)Reading (process)PsychologyDisciplineTeaching methodTheme (computing)LinguisticsExtensive readingPedagogyMathematics educationComputer scienceSociologyWorld Wide Web

Abstract

fetched live from OpenAlex

An adaptation of the traditional literary concept of close reading was developed for use in a largely multilingual classroom in which both first language (L1) and second language (L2) students were struggling to comprehend theoretical, lexically dense texts in English. This simplified method of reading a text iteratively and critically is proving helpful in encouraging student compliance with reading assignments as well as progress in academic writing capabilities. This method was developed through collaboration between an East Asian Studies (EAS) department and the university’s English Language Learning (ELL) specialist. The large lectures are supplemented by small-group discussions with teaching assistants (TAs), who also engage in reflective professional development workshops to build their own skills in teaching close reading. Materials generated for both students and faculty through this initiative are being disseminated in other departments, and TAs have noted an overall improvement in students’ fulfillment of reading assignments as well as their ability to generate written arguments.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.061
GPT teacher head0.393
Teacher spread0.332 · 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