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Record W7096274813

Canadian Journal of Practice-Based Research in Theatre Vosters Volume 5.1, 2013 1 IMPACT LAB AND THE HALPRIN LIFE ART PROCESS: A PRACTICE-BASED APPROACH TO CREATIVE, AFFECTIVE, AND POLITICAL

2016· article· en· W7096274813 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRepatriationAfghanPoliticsFellRealmIraq war
DOInot available

Abstract

fetched live from OpenAlex

This is the first time in my life I am going to be a part of something much bigger than myself, and I don’t really know what to expect. — Christina Demunda, Impact Lab, 2011 During Canada’s decade-long engagement engagement in the post-9/11 US-led invasion of Afghanistan 158 Canadian military personnel were killed. Each of these deaths has been memorialized in the media, through formal and popular repatriation ceremonies, and via the renaming of sections of the repatriation route to the “Highway of Heroes ” and the “Route of Heroes. ” Unlike Canada’s military dead, there are no exact numbers for Afghans who were been killed or who died as a result of war-related causes during this time. While, in part, the lack of an accurate accounting of Afghan dead is a result of the US/NATO no-body count policy,1 it is also reflective of a “differential distribution of grievability across populations ” wherein Western lives are deemed grievable while the lives of non-Western “others ” remain outside of the realm of grievability (Butler 2010, 24). From July 1 (Canada Day) 2010 through July 1 2011, I performed Impact Afghanistan War [Impact] a alternative memorial project in which I fell 100 times a day in a public space for one year. Each of Impact’s 36,600 falls was done in recognition of one of the tens of thousands of unnamed and uncounted Afghan dead. Impact was an attempt to reach beyond the numbness produced by abstract numbers, political ideologies, and media spectacularization, and to allow myself, and invite others, to be impacted. Though primarily a solo performance memorial, others occasionally joined me when I fell: Once, as an organized group fall on October 7, 2010, the 9th anniversary of the US-led invasion of Afghanistan; from time to time, as a spontaneous gesture; in the case of friend and colleague, Bradley High, as an almost weekly participant; and, at a

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)0.019
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.683
Threshold uncertainty score0.631

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.007
Science and technology studies0.0150.016
Scholarly communication0.0160.004
Open science0.0050.015
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.1490.021

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 head0.050
GPT teacher head0.324
Teacher spread0.274 · 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 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
Published2016
Admission routes1
Has abstractyes

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