Utilizing artificial intelligence and social media: Transforming public administration decision mak-ing and redefining the social responsibility landscape
Bibliographic record
Abstract
Company activities can yield both favorable and adverse outcomes. Among the detrimental effects are various environmental crises stemming from companies' failure to responsibly manage and uphold ethical business practices within their operational environment. This study aims to scrutinize the impact of artificial intelligence and social media on augmenting public administration decision-making and its repercussions on corporate social responsibility within enterprises situated in Sidoarjo, East Java Province, Indonesia. The sample comprises 130 respondents representing companies actively engaged in corporate social responsibility endeavors. Employing purposive sampling technique, data collected from surveys were subjected to Structural Equation Modeling-Partial Least Squares (SEM-PLS) analysis. The research outcomes and data analysis reveal that artificial intelligence significantly and directly influences corporate decision-making as well as public administration of social responsibility. Likewise, the utilization of social media by both corporations and government exhibits a direct and positive correlation with corporate decision-making and corporate social responsibility. Public administration decision-making also demonstrates a direct and significant impact on corporate social responsibility within companies in Sidoarjo, East Java Province, Indonesia. Moreover, public administration decision-making partially mediates the effects of artificial intelligence utilization and social media utilization on public administration of social responsibility within companies in Sidoarjo, East Java Province, Indonesia.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".