MétaCan
Menu
Back to cohort
Record W4286714786 · doi:10.3138/jeunesse.11.1.59

“Are You Preparing for Another War?”: Un/Just War and the Hunger Games Trilogy

2019· article· en· W4286714786 on OpenAlexvenueno aff
Roxanne Harde

Bibliographic record

VenueJeunesse Young People Texts Cultures · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTrilogyJust war theoryScholarshipNarrativeRomanceLawPlot (graphics)Jus ad bellumSociologyVietnam WarSpanish Civil WarPolitical scienceLiteratureArt

Abstract

fetched live from OpenAlex

Drawing from the body of just war theory, this article analyzes Suzanne Collins’s discussion of warfare in the Hunger Games trilogy, tracing the ways in which decisions about war unfold along the lines of the love triangle plot involving Peeta and Gale. Although there are important issues about social injustices driving the trilogy, a fair amount of scholarship has focused on romance in the novel. The more interesting tension in the narrative is not Katniss’s romantic entanglements but the conflict among Peeta’s adherence to the principles of just war, Gale’s disregard of them, and Katniss’s continuing moral dilemmas about them. Arguing that Katniss’s deliberations about war—both joining the rebellion and fighting in the war against the Capitol—are aligned with the foundational principles of just war theory, this essay traces the mandates of jus ad bellum as set out in The Hunger Games and Catching Fire, where Katniss, influenced by both Gale and Peeta, considers rebellion, and then reads in Mockingjay the ways in which the ethical demands of jus in bello lead her to choose Peeta.

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)0.005
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 score0.020
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0200.043
Scholarly communication0.0110.010
Open science0.0010.006
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.241
Teacher spread0.230 · 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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJeunesse Young People Texts CulturesSame topicThemes in Literature AnalysisFrench-language works237,207