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Record W3135106058 · doi:10.29173/iasl7880

Allusions, Illusions and Learning: Integrating story into the broader curriculum

2021· article· en· W3135106058 on OpenAlexvenueno aff
Julie Granger

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsPresentation (obstetrics)Unit (ring theory)CurriculumLiteracyReading (process)Context (archaeology)IllusionCritical literacyPedagogyMathematics educationPsychologyComputer scienceLinguisticsCognitive psychologyHistory

Abstract

fetched live from OpenAlex

The workshop will be a visual presentation covering the rationale for a unit of research that involves integrated learning/curriculum and concentrates on critical literacy. The presentation will also involve a complete example unit and accompanying course booklet. Participants will be involved in a handson evaluation of the unit presented and assess its suitability to adapt to other “core literature genres”. The unit of work involves the “multiple faces of literacy: Reading. Knowing. Doing.” at the practical teaching level.
 The aim is to show how to use texts from “classic fiction” in research units, as vehicles for learning, not only about the texts themselves, but also about their wider cultural significance and associated literary forms, as well as these stories represented in other media. The unit attempts to place everything examined in a meaningful context and to provide opportunities for students with diverse learning styles to enjoy the unit and to succeed. The objective is to make classic stories the integrating vehicles for learning, and so change them from literary “illusions” to “allusions” once more, therefore showing that the “critical literacy” approach can use traditional literature or story in teaching.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, 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 score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.244
Teacher spread0.226 · 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
Published2021
Admission routes1
Has abstractyes

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