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Record W4414127708 · doi:10.29303/jgn.v7i2.556

Upaya Peningkatan Kesadaran Masyarakat Melalui Sosialisasi Keselamatan Berlalu Lintas dan Tertib Parkir di Kabupaten Bombana

2025· article· en· W4414127708 on OpenAlexaff
Ramsi Ramsi, Andi Firman, Mursal Salam, Marthen PS, Hado Hado, Adris Ade Putra, Try Sugiyarto Soeparyanto, La Ode Muhamad Nurrakhmad Arsyad, Nasrul Nasrul, Ringo Taufan Laode, Maudhy Satyadharma

Bibliographic record

VenueJurnal Gema Ngabdi · 2025
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Management
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsSocializationService (business)Community serviceOrder (exchange)Distribution (mathematics)

Abstract

fetched live from OpenAlex

The development of transportation grow, thus affecting the motorized vehicles number, especially motorbikes. This has consequences, namely traffic accidents. This provides encouragement for many parties to continue to socialize community service activities in order to encourage increased awareness of drivers in orderly traffic and also orderly parking of vehicles. The implementation of this traffic safety and orderly parking socialization activity was carried out for four days (February 10-13, 2025) in Bombana Regency with 80 service users who received socialization in the form of brochures and flyers by the socialization team. The results of the socialization evaluation obtained through the distribution of questionnaires showed that there was an increase in understanding of service users by 55.4% after socialization event, which indicates the effectiveness of understanding the contents of the material in encouraging increased understanding and awareness in safe, comfortable, safe traffic and obeying traffic rules.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0240.005

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 head0.039
GPT teacher head0.421
Teacher spread0.382 · 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 designNot applicable
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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