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Record W4200483305 · doi:10.6000/1929-4409.2021.10.189

The Role of Family Disintegration in Piracy of Electronic Games “A Field Study”

2021· article· en· W4200483305 on OpenAlexvenueno aff
Heba Atef El Sayed Mohmoud Awad

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2021
Typearticle
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFeelingAngerField (mathematics)IntimidationOriginalityPsychologyControl (management)Social psychologyComputer scienceMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Purpose: Recognizing the technological effects of family disintegration.
 Method: Human field: a sample of the dangerous electronic games players, including "4" players who are still alive, and "5" players who committed suicide. Methods and Tools: The Case study method, Ethnographic method, Descriptive approach, and Interview. The research type is Analytic, and the theoretical framework is Postmodernism Theory.
 Originality: The researcher tries to provide a comprehensive view of how electronic games piracy on their players and pushes them to suicide, in the presence of the family disintegration element.
 Findings: family disintegration was the main reason for children’s addiction to electronic games. Thus, electronic games were like escaping from reality and living in imagination, and spending free time. Also, electronic games were a means that absorbed the negative charge and feelings of anger among the children instead of the family. There are many types of piracy on players: (programming for the mind, charging with negative thoughts, threatening to kill parents, an emotional challenge to the teenager, blackmail and intimidation, or with talismans).
 Conclusion: a person can control another, to the extent that this other person allows this person to control him. Do not allow a game administrator to control you, activate Cybersecurity.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.134

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 head0.027
GPT teacher head0.318
Teacher spread0.291 · 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 designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

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