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Record W2804844378 · doi:10.5539/ells.v8n2p92

Mechanism of Power

2018· article· en· W2804844378 on OpenAlexvenueno aff
Mohamad Haj Mohamad

Bibliographic record

VenueEnglish Language and Literature Studies · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPower (physics)TrilogyState (computer science)Government (linguistics)Mechanism (biology)DemocracyPoliticsSociologyAuthoritarianismLaw and economicsPolitical scienceLawComputer scienceEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

This paper attempts an examination of the concept of the mechanism of power in Suzanne Collins’ trilogy The Hunger Games. The author conducts an analytical approach to the way power is practiced, the measure that helps establish a clandestine of power relations affecting people’s life, mentality of thinking, politics and even economy.The introduction delves to present a definition of power and its concept and how it gets activated and why. Power, according to Collins is the backbone and even the elixir of life upon which the very survival of the authoritarian state headed by President Snow depends. The paper goes on to explicate the need for keeping power in place to secure the government’s grip on power. Mechanism of power as shown in the novel works on so many levels. Divide and rule marks the first and most necessary and effective means as a divisive policy aiming at preventing any potential unity among people who might employ this unity to rise up against the totalitarian government. Media and sport and economic factors are used effectively to ensure government’s control on man’s mind, body and soul and intimidate them whenever needed. Collins presents power and its mechanism as the sole relation between people and government in the novel. Absence of democratic rule in Panem, or, American states, leaves power as the only means to describe the social bond between man and state, a bond that is unilaterally respected and practiced.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.401
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.241
Teacher spread0.233 · 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 teacher head, 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

Citations1
Published2018
Admission routes1
Has abstractyes

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