The role of social communication in shaping territorial responsiveness and loyalty and its impact on sustainable territorial performance
Bibliographic record
Abstract
This study aims to analyze the effect of social communication on sustainable territorial performance at the Iskandar Muda Military Command in Aceh. The research sample consisted of 326 Village Supervisors (Babinsa) who were recruited through an invitation to complete a questionnaire distributed via official TNI communication channels with a survey link on Google Forms. Data were collected using a structured questionnaire designed to gather demographic information of respondents and research variables, including social communication, sustainable territorial performance, territorial responsiveness, and territorial loyalty. The questionnaire consisted of two parts, utilizing a 5-point Likert scale to measure respondents' levels of agreement with the statements. Data analysis was conducted using Structural Equation Modeling (SEM) with a Partial Least Squares (PLS) approach. The results indicated that territorial responsiveness and territorial loyalty do not mediate the relationship between social communication and sustainable territorial performance. However, both variables have a direct positive and significant impact on sustainable territorial performance. Additionally, social communication was found to have no direct effect on sustainable territorial performance, territorial responsiveness, or territorial loyalty. These findings provide important insights into the relationship between social communication and territorial performance, as well as implications for management practices in military settings.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".