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Record W3035466437 · doi:10.36510/learnland.v13i1.1015

Performing School Failure: Using Verbatim Theatre to Explore School Grading Policies

2020· article· en· W3035466437 on OpenAlexvenueno aff
Rebecca Sánchez

Bibliographic record

VenueLEARNing Landscapes · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicEducational Assessment and Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsPunitive damagesGrading (engineering)Qualitative propertyQualitative researchPsychologyPedagogyTollMathematics educationSociologyPolitical scienceComputer scienceEngineeringSocial scienceMedicine

Abstract

fetched live from OpenAlex

This article describes how dramatic writing and performance practices can be used to reshape qualitative interview data into a verbatim theatre performance with the intent of drawing attention to social movements in education. The performance described in the article reveals the consequences of a punitive educational policy agenda and addresses the emotional toll school grading and other neoliberal policies have had on teachers at a school in the southwestern United States. A primary objective is to examine and explore how verbatim dramatic writing and performance tactics can amplify current issues and social dilemmas and evoke an emotional response in the absence of dramatic action. The methods of writing a script from qualitative data are presented for other scholars and educators who intend to create performances from data.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.137
GPT teacher head0.386
Teacher spread0.249 · 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; both teacher heads agree on what is shown here.

Study designObservational
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
Published2020
Admission routes1
Has abstractyes

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