HUBUNGAN ANTARA PENGAWASAN DAN LINGKUNGAN KERJA DENGAN EFEKTIVITAS KERJA PEGAWAI KANTOR WILAYAH KEMENTERIAN AGAMA PROVINSI SULAWESI UTARA
Bibliographic record
Abstract
This research aims to determine the relationship between (1) controlling with work effectiveness, (2) work environment with work effectiveness, and (3) the relationship between controlling and work environment with work effectiveness altogether. Methods this research used a survey with the correlation approach. The sample of this research use 60 responden, and 20 responden for testing, and selected based on simple random sampling. Study toward regional office employees ministry religion province North Celebes.The technique of data collecting was using the instrument in the form of questionnaire. This instrument is calibrated with the test item validity and reliability coefficients. To test the validity of three variables (controlling, work effectiveness and work environment) using Pearson Product Moment correlation, while the reliability coefficient is calculated three variables (controlling, work effectiveness and work environment) using the formula of Cronbach Alpha. The results of this research are first, there are positive relationships between controlling and work effectiveness, which can be given on the correlation coefficient with the regression equation. Secondly, there is a positive relationship between work environment with work effectiveness that can be given on regression correlation coefficient with regression equation. Third, there is a positive relationship between controlling and work environment together with work effectiveness, which can be given on the correlation coefficient by multiple regression equation.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".