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Record W4387738674 · doi:10.5070/nc34262326

Collaborative Teaching and Creative Assignments Using Contemporary Adaptation

2023· article· en· W4387738674 on OpenAlexaff
Brendan O’Connell, Alexandra Colby

Bibliographic record

VenueNew Chaucer Studies Pedagogy and Profession · 2023
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsAdaptation (eye)Value (mathematics)Meaning (existential)Creative writingCurriculumProcess (computing)Dynamics (music)Creative workFocus (optics)SociologyPedagogyLiteraturePsychologyEpistemologyVisual artsComputer scienceArtPhilosophy

Abstract

fetched live from OpenAlex

In this article, we share our perspectives (as teacher and student) on the role of modern adaptations of Chaucer in teaching and assessment, with a particular focus on the role such adaptations play in supporting the use of creative writing-based assignments in a medieval literature course. We describe our experience of an assessment composed of a creative exercise combined with a critical commentary, and discuss how the incorporation of modern adaptations of medieval texts into the medieval literature curriculum underpins and supports this assessment type. Our account demonstrates that the process by which the meaning of literary texts is generated is iterative and collaborative, a point we hope to underscore through our collaboration on this piece. We hope the experience we describe will foreground the value of dialogue in the processes of teaching, assessment, and feedback, and also highlight the role of modern adaptations in supporting students to recognise and articulate the value of their own creative and critical work within a longer tradition of literary and scholarly responses to medieval literature.

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)0.000
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.274
GPT teacher head0.530
Teacher spread0.256 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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