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Record W7065886481

The Framing of Myth in the Creation of a Palestinian Identity: Hamas, Fatah and Childrenâs Media

2011· other· en· W7065886481 on OpenAlexvenueno aff

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2011
Typeother
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsMythologyBattlePoliticsHEROFraming (construction)Identity (music)Ethnography
DOInot available

Abstract

fetched live from OpenAlex

This thesis is an exploratory examination of identity construction and children’s media, with a focus on the Palestinian political groups of Fatah and Hamas. It looks at how children’s media are framed within the context of the Arab-Israeli conflict. It examines how internal and external social factors contribute to identity formation and the interaction among these elements during times of conflict and war.\nThis thesis hypothesizes that both Fatah and Hamas use various myths to differing degrees in order to frame their conception of a Palestinian identity. Specifically, it explores the use of the Myth of Battle, the Myth of Hero, the Myth of Victim, the Myth of Religion, the Myth of Land and the Myth of Other. It seeks to determine which of these myths each group emphasizes through a qualitative and quantitative visual ethnographic content analysis. \nThe quantitative analysis uncovered interesting, albeit not statistically significant, differences between Fatah’s and Hamas’ use of all of the myths in their videos. Specifically it found that both groups made equal and great use of the Myth of Religion; that Hamas produced the videos with the greatest focus on the Myth of Battle and the Myth of Hero; and that neither group greatly emphasized the Myth of Victim, the Myth of Land or the Myth of Other. Finally, the analysis discovered positive correlations between the Myth of Hero and the Myth of Battle as well as between the Myth of Battle and the Myth of Other.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score0.900

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)0.0000.000

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 head0.003
GPT teacher head0.136
Teacher spread0.132 · 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 designObservational
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
Published2011
Admission routes1
Has abstractyes

Explore more

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