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Record W7066319767

HUBUNGAN ANTARA SIKAP KOMPETITIF TERHADAP KUALITAS PERSAHABATAN DIMODERATORI KEMATANGAN EMOSI

2025· dissertation· en· W7066319767 on OpenAlexaboutno aff

Bibliographic record

VenueUMM Institutional Repository (University of Maine at Machias) · 2025
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsModerationFriendshipFeelingAffectionMaturity (psychological)Reliability (semiconductor)
DOInot available

Abstract

fetched live from OpenAlex

Adolescence is a stage of development in the age range of 13-16 years which is characterized by starting to be faced with increasingly complex milestones. This causes the quality of friendship. One of the causes of friendship quality in adolescents is related to competition or how individuals describe competitive attitudes. This will have an impact on one of the less than optimal achievement of milestones at this time. One mechanism that can shape positive feelings is emotional maturity. Emotional maturity as a moderator is expected to strengthen the relationship between competitive attitudes and friendship quality. The research method used is quantitative with a moderation research design. This study aims to examine the role of emotional maturity as a moderator variable by involving a total of 150 subjects. The research instruments used were Competitive Attitude Scale (CAS) with a reliability of 0.908, McGill Friendship Questionnaire-Friendship Function (MFQ-FF) with a reliability of 0.899, Emotional Maturity Scale (EMS) with a reliability of 0.918.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0370.012

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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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