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Record W2889609788 · doi:10.25071/1916-4467.40353

Reframing Syllabi as Aesthetic Encounters

2018· article· en· W2889609788 on OpenAlexaffvenue
Michael Lockett, Gabriel T. W. Wong

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsEmily Carr University of Art and Design
Fundersnot available
KeywordsSyllabusCurriculumDisciplineSociologyPedagogyAestheticsArtSocial science

Abstract

fetched live from OpenAlex

This academic work, which is comprised of three artefacts, responds to Maxine Greene’s “Spaces of Aesthetic Education” (1986). The main artefact is a syllabus, from an era of increasingly standardized syllabi, which imagines its aesthetic and educative attributes otherwise. In doing so, it reconsiders the kinds of learning a syllabus might prompt. It stems from a series of conversations we, the co-creators, shared about the ways curricular structures can come to prompt critical, creative and aesthetic attention. We had pursued those intersections in the past from our respective disciplinary perspectives and decided to collaborate on an art/research project, one that could inform and provoke a series of future curricular conversations. As our work unfolded, we spoke of curricular experiences that were meaningful and those were not and tried to articulate what we meant by “aesthetic experience”. We came to lament institutional demands for standardized curricular documents in our respective teaching contexts, especially mandated templates for syllabi. We wondered about the educative and aesthetic consequences of limiting their expression to a series of prescribed descriptors. Eventually we had an opportunity to experiment with the form through a fourth-year course on curriculum theory and practice, an ideal venue for introducing a parallel, yet supplementary, syllabus. That syllabus is displayed in full in this issue of the journal. It is also accompanied by an audio file and a corresponding transcription. Through that recording, we address some of the aesthetic considerations we incorporated into the design, delineate certain curricular choices, and explain the artefact’s discursive significance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0090.018
Scholarly communication0.0140.011
Open science0.0020.014
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0070.002

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 head0.022
GPT teacher head0.278
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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