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Record W4386506198 · doi:10.5430/wjel.v13n8p138

Overcoming Fear of Abuse Through Socialization in Samra’s A Good Wife Escaping The Life I Never Chose

2023· article· en· W4386506198 on OpenAlexvenueaboutno aff
R. Mary, N. S. Vishnu Priya

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologySocializationSocial psychologyWifeContext (archaeology)AutonomyEmpowermentMoralityPower (physics)ChoseSociologyLawPolitical science

Abstract

fetched live from OpenAlex

Samra Zafar, a young and inspiring award-winning speaker in Canada, recounts her real-life experiences in her literary work, "A Good Wife: Escaping the Life I Never Chose." This poignant memoir chronicles Samra's journey through the hardships she faced in pursuing her education and her abusive marriage. From her teenage years, she endured turbulent experiences that instilled fear within her. However, she came to realize that her dreams were more significant than succumbing to her fears. By pursuing education and utilizing it as a tool for empowerment, she overcame these fears. Fear is deeply interconnected with human emotions and sentiments, as Friedrich Nietzsche once stated, "Fear is the mother of morality," and this quote resonates with Samra's experiences. Raised in a religiously rooted environment, Samra was influenced by moral and religious beliefs. By utilizing concepts from social psychology and theories of socialization, this paper aims to explore how Samra's experiences with social interactions, support systems, and self-perception contributed to her ability to overcome the fear of abuse and reclaim her autonomy. The analysis provides valuable insights into the transformative power of socialization in the context of overcoming fear and breaking free from abusive relationships.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.306
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.308
Teacher spread0.283 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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