The Influence of Information Acceptance on Information Use Performance in the Case of Vulnerable Classes
Bibliographic record
Abstract
This study analyzed the effect of information acceptance on the information use performance for the vulnerable groups. The sample group was classified into general public group and vulnerable groups residing in Korea, and the vulnerable groups were divided into the disabled, the low - income group, the elderly group and the farmers. The characteristics of the vulnerable groups were selected by the competence level, use motive, and use attitudes and the correlation between each factor and use utilization was analyzed. In all groups, the use motive and the use attitude showed a strong positive correlation with the use performance of .682. In particular, the information use attitude and the information use performance were closely related. The correlation between the use attitude and use performance of the vulnerable groups were investigated in the order of lower grade (.695), farmers & fishermen (.688), elderly (.674), and disabled (.672). In conclusion, this study shows that if use attitude is high, use performance is high. Therefore, in order to expand the acceptance of information on vulnerable groups, various programs that can increase the use attitude of information should be introduced. Through this, it was found that the vulnera-ble groups could induce the use attitude of information and the satisfaction of the use performance, and ultimately the digital information divide could be reduced by improving the information acceptance of the vulnerable groups. This study has limitations in securing various contents of inquiry based on questionnaire survey. It is necessary to analyze various factors besides factors selected as environmental fac-tors of information acceptance as characteristics of vulnerable groups.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".