The influence of social media and big data utilization in improving networking and its implications on the quality of civil service decisions in Surabaya city
Bibliographic record
Abstract
The purpose of this study is to analyze the influence of social media and big data utilization in improving networking and its implications on decision quality in civil servants in Surabaya City, East Java Province, Indonesia. The sample in this study was 125 respondents consisting of several Civil Servants in the Surabaya City Government, East Java Province, Indonesia. The sampling technique used the random sampling. Data collected through questionnaires were then analyzed using SEM-PLS. The results of the study and data analysis showed that: Utilization of Social Media directly has a positive and significant effect on Networking; Big Data directly has a positive and significant effect on Networking; Utilization of Social Media directly has a positive and significant effect on Decision Quality; Government Big Data directly has a positive and significant effect on Decision Quality; Networking directly has a positive and significant effect on Decision Quality in Civil Servants of Surabaya City, East Java Province, Indonesia. Networking is able to indirectly mediate the utilization of social media and big data on decision quality in civil servants of Surabaya City, East Java Province, Indonesia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".