Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the relationship and interactions among trust, information and communication technologies (ICT) and country income levels. Design/methodology/approach This study adopts the standardization method by Osberg and Sharpe (2005) and analyzes the changes in global trends, coefficient of variations, and correlations. The statistical data consist of panel data for 28 countries from 2007 to 2014. Findings Trust in people (TP) and institutional confidence (IC) have different shapes of movement over the period and the change speed of IC has decreased much faster than that of TP. While TP in high income countries is positioned in relatively high ranks, IC of middle income countries tends to be ranked in higher ranks. While the telecommunication infrastructure index (TII) has continuously increased in all countries for the entire period, open service index (OSI) has not increased at the same rate since improving OSI is not easier than TII. As OSI increases, IC may affect an increase to a certain point and then decrease in an inverted U-shape. The result of this relationship emphasizes on the importance of OSI along with TII in building trust, particularly with institutions. Research limitations/implications The examination of the relationship of trust, ICT and income in quantifiable values can contribute to understanding the direction of movement and change speed toward trust building with people and institutions. Practical implications To promote levels of trust, countries should consider different strategies for growing TP and institutions and concentrate on improving ICT-mediated services more than installing ICT facilities. Originality/value Quantifying the interactions of a qualitative concept of trust with ICT facilities, online services, and income levels presents an in-depth analysis of TP and with institutions.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".