Assessment of trust level based on 3d models of social relationships factors in public institutions
Bibliographic record
Abstract
Trust is a key attribute of social cohesion that is a major phenomenon in social relationships. This research aims to trust levels in social relationships and understand how social relationships affect trust levels. This research uses the theory of social relationships as an understanding of the level of trust in modern organizations, the theory of trust based on three dimensions namely trust in information, motives, and competence. Statistical descriptive qualitative research method is used as an approach supported by Delphi analysis, Analytical Hierarchy Process (AHP), and TOPSIS (Technique for Others Preference by Similarity to Ideal Solution). In identifying factors in the social relationship between policy and community, nine social relationship factors were obtained, including Communication (A1); Trust (A2); Cultural (A3); Procedural Justice (A4); Problem-Solving (A5); Transparency (A6); Engagement (A7); Collaboration (A8); Empowerment (A9). On the one hand, in the context of relative importance, the weight value at the criteria level is trust in Information (C1) (19.8%); Trust in Motives (C2) (31.2%); Trust in Competence (C3) (49%). Based on the results of the 3D trust level-based mapping analysis on social relationships, of the nine alternatives there are no factors with complete level (level 5) and Ignorance (Level 1). Overall, there are two alternative social relationship factors with high trust level (level 4), namely Trust (A2) and Collaboration (A8). These findings suggest that social relationship factors, such as trust (A2) and Collaboration (A8), play an important role in increasing the trust value of institutions related to trust from the community.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".