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Record W2617837726 · doi:10.5539/ies.v10n6p44

The Impact of School Bullying On Students’ Academic Achievement from Teachers Point of View

2017· article· en· W2617837726 on OpenAlexvenueno aff
Hana Khaled Al –Raqqad, Eman Saeed Al-Bourini, Fatima Mohammad Al Talahin, Raghda Michael Elias Aranki

Bibliographic record

VenueInternational Education Studies · 2017
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAcademic achievementSample (material)Mathematics educationPerspective (graphical)Affect (linguistics)Descriptive researchSociologyMathematicsSocial science

Abstract

fetched live from OpenAlex

The study aimed to investigate school bullying impact on students’ academic achievement from teachers’ perspective in Jordanian schools. The study used a descriptive analytical methodology. The research sample consisted of all schools’ teachers in Amman West Area (in Jordan). The sample size consisted of 200 teachers selected from different schools from Amman West area in Jordan. A self-administrated questionnaire was designed according to research objectives and hypotheses and distributed over research sample subjects. All distributed questionnaire were collected. They were, coded and analyzed by using SPSS version 18. The research results indicated that school bullying exists in all schools regardless if they are governmental or private ones. The study also concluded that school bullying affect student’s academic achievement either victims or the bullies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.094
GPT teacher head0.487
Teacher spread0.393 · 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 source (direct Gemma or distilled Codex), 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

Citations94
Published2017
Admission routes1
Has abstractyes

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