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Record W4319727206 · doi:10.26034/vd.jrea.2023.3586

Arts education in Ireland: Visions of a performative teaching, learning and research culture

2023· article· en· W4319727206 on OpenAlexaff
Erika Piazzoli, Manfred Schewe

Bibliographic record

VenueJournal de recherche en éducations artistiques (JREA) · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsTrinity College
Fundersnot available
KeywordsPerformative utteranceVisionArgument (complex analysis)SociologyCurriculumThe artsPedagogyContext (archaeology)DramaAestheticsVisual artsArtHistoryAnthropology

Abstract

fetched live from OpenAlex

In this paper we offer an overview of the arts in education in Ireland, making a case for a performative approach. We discuss theory, research and practice – situating the developments in Ireland within a multicultural context. We begin by offering our own definition of Arts (in) Education research, drawing on seminal policies and interrogating various art classifications, from a historical and etymological perspective. Next, we consider their impact at a global and regional level and we examine how policy has influenced the curriculum. Following this, we reflect on the ramifications of theatre and drama in education, highlighting the fragmentation in the field. In the main argument of the paper, we make a case for performative education, illustrating our vision for performative language teaching and research. We suggest that theory of aesthetic experience can be a useful framework, and close with remarks on our contemporary society.

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)0.040
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0120.114
Scholarly communication0.0450.017
Open science0.0030.025
Research integrity0.0050.012
Insufficient payload (model declined to judge)0.0040.001

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 head0.239
GPT teacher head0.505
Teacher spread0.266 · 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.

Study designQualitative
DomainEvaluation
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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