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Record W4251781793 · doi:10.24036/student.v4i1.708

[no title]

2020· article· W4251781793 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJURNAL BUANA · 2020
Typearticle
Language
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsSample (material)Data collectionSocioeconomicsQualitative researchQualitative propertyGeographyPopulationEnvironmental healthSociologyMedicineMathematicsStatisticsSocial science

Abstract

fetched live from OpenAlex

This study aims to obtain data and information on 1) income levels 2) educational conditions 3) living conditions 4) health conditions in Non-Metallic Mineral Mining in the Village of Gunung Sarik, Kuranji District, Padang City..This research uses a mixed method, which is a method that combines quantitative and qualitative approaches. The study population was Non-Metallic Mineral Miners in Gunung Sarik Sub-District, Kuranji District, Padang City. The quantitative research sample was 66 workers. Qualitative data collection techniques using observation and questionnaire distribution. Whereas the qualitative research sample is the owner of a CV of 1 person. For qualitative data collection techniques using interviews.The results showed that: (1) the level of income of workers at the mine was Rp 2,500,000- Rp 3,500,000 per month (65.1%) (2). the education conditions of mine workers are relatively low with the majority of junior high school graduates (39.3%). (3) the status of houses occupied by mining workers almost all belong to sendri (50%), almost every house is equipped with PLN electricity. (4) The health condition of mine workers is very good in working activities (48.5%).

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.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.323
Teacher spread0.256 · 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