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Record W2410932517 · doi:10.31269/triplec.v14i1.696

Class Struggle in Contemporary Films: "Hunger Games" vs. "Arrow, The Ultimate Weapon"

2016· article· en· W2410932517 on OpenAlexaff
Dal Yong Jin

Bibliographic record

VenuetripleC Communication Capitalism & Critique Open Access Journal for a Global Sustainable Information Society · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Culture and Media Studies
Canadian institutionsSimon Fraser University
FundersAcademy of Korean Studies
KeywordsArrowCapitalismSociologyOrder (exchange)Meaning (existential)Class (philosophy)Marxist philosophyAestheticsMedia studiesEpistemologyLawPolitical scienceComputer sciencePoliticsArtPhilosophyEconomics

Abstract

fetched live from OpenAlex

By critically engaging with Marxist notion of class struggle in contemporary films utilizing the bow and arrow as their signifier, this paper textually analyzes two films in order to find distinctive characteristics of Western movies and non-Western movies. Since the textual analysis becomes very important to understanding how media texts might be used in order to make sense of the world we live in—meaning it is significant to contextualize it within our life and/or society, this paper investigates the ways in which the major themes have developed and what their representations are. It therefore compares and contrasts these movies in terms of their major themes, in particular the ways in which these two films portray capitalism, either internally or externally. It especially examines how the bow and arrow symbolize class struggle either within a country or between countries in order to map out the major differences that the arrows represent.

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)0.001
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.007
Scholarly communication0.0060.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.432
Teacher spread0.375 · 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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