Improving Effectiveness of Rural Information and Communication Technology Offices: The Case of Qazvin Province in Iran
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The information and communication technology (ICT) offices in rural areas of Iran have been developed as government provided counters under a national project in the past decades. The rural ICT offices were expected to benefit the rural people in various socio-economic dimensions such as health, social connectivity, crop diversity, agricultural productivity, occupational capability and the lifestyle in general. However, these middle range offices in Iran did not perform as expected, and thus they require an urgent restructuring to boost up their performances and to enhance their acceptability. This study investigates the effectiveness of the ICT systems and services in place in the Qazvin province of Iran with the purpose of identifying the major requirements needed to fix up the system. The focus of this study was around 10,000 people organised through rural ICT agents and their users in the rural area of Qazvin. The survey involves 138 rural ICT offices operated by 103 cooperative agents. Of them, 16 rural ICT offices were selected randomly, and 165 rural users connected with the selected offices were interviewed by the research team. Collected data have been analysed with structural equation modeling. The study shows that education, policy and management requirements deserve the highest attention, and therefore the best ways to improve the effectiveness of rural ICT offices. This study suggests that the effectiveness of rural ICT offices can be improved significantly through providing in-service education for ICT experts, arranging regular training programme for ICT office agents and using mass media to educate villagers on various aspects of ICTs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it