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Record W2325270724 · doi:10.5539/ijps.v8n2p14

Aggression Behaviors in Children with and without Hearing Impairment

2016· article· en· W2325270724 on OpenAlexvenueno aff
Ayhan Babaroğlu

Bibliographic record

VenueInternational Journal of Psychological Studies · 2016
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsnot available
Fundersnot available
KeywordsAggressionPsychologyDevelopmental psychologyAudiologyMedicine

Abstract

fetched live from OpenAlex

<p style="margin: 0cm 0cm 0pt; text-align: justify; line-height: normal;"> </p><p>The aim of this study is evaluating aggression behaviors of children with hearing impairment and comparing them with their peers who do not have any kind of hearing problems. For this purpose, 81 children with hearing impairment and 80 children with no hearing problems between the ages of 10-17 years were included in the study (a total of 161 children). The data of the study were obtained by Buss-Perry Aggression Questionnaire and the General Information Form. According to the results of this study, the total aggressiveness varies depending on age and hearing children show less aggressive behaviors as they get older; however, in children with hearing impairment, no difference was observed in their aggressive behaviors depending on their ages; gender creates a difference in total aggression behaviors in both children groups with and without hearing impairment and boys show more aggression behaviors compared to girls; children, who received pre-school education, with impairment show more aggressive behaviors, the existence of another hearing impairment family member increases the physical and indirect aggression behaviors of children with hearing impairment.</p><p style="margin: 0cm 0cm 0pt; text-align: justify; line-height: normal;"><span style="font-family: 'Times New Roman','serif'; font-size: 10pt; mso-ansi-language: EN-US;" lang="EN-US"><br /></span></p>

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.239

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 head0.046
GPT teacher head0.397
Teacher spread0.351 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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