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Record W4320170156 · doi:10.32920/21950399

Towards a Theatre of Global Empathy: Imagining Otherness in the War on Terror

2023· preprint· en· W4320170156 on OpenAlexaboutno aff
Matt Jones

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMythologyEmpathyDysfunctional familyFellWar on terrorMedia studiesAl qaedaHistoryPsychologySociologySpanish Civil WarPolitical scienceLawArtPsychoanalysisCriminologyTerrorismLiteratureSocial psychologyCartographyGeography

Abstract

fetched live from OpenAlex

In Hannah Moscovitch’s play about the Canadian mission in Afghanistan, three soldiers stand and address the audience as if they are being interviewed by a journalist. Who are they talking to? The story they tell is about the violence they endured and committed overseas as well as the dysfunctional sexual and psychological states they fell into as they were pushed to their limits. As they struggle to rationalize the decisions they made to an audience they perceive as potentially hostile, it becomes clear that they are speaking across a chasm of misunderstanding. As the play’s curt title, This Is War, suggests, their stories strip away the sanitized mythologies that surround humanitarian intervention to reveal the brutal truth that former General Rick Hillier introduced us to in a 2005 CTV News story on hunting al Qaeda in Afghanistan: the job of the Canadian Forces these days is simply “to kill people.”

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)0.003
metaresearch head score (Gemma)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.066
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0210.045
Scholarly communication0.0120.007
Open science0.0010.007
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0070.001

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 head0.121
GPT teacher head0.324
Teacher spread0.203 · 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
Published2023
Admission routes1
Has abstractyes

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