The impact of social capital on children's (6-12) use of ICT in urban communities: A field survey from Zhengzhou City, Henan Province
Bibliographic record
Abstract
<div style="text-align: justify">\n\t<strong>Purpose</strong>: This paper focuses on the impact of social capital on urban children&#39;s use behavior of information communication technology (ICT).<br />\n\t<br />\n\t<b>Design/methodology/approach</b>: Using the field survey and in-depth interviews, we interviewed 40 children aged 6 to 12 and their parents from a staff residential quarter of the Zhengzhou University-&ldquo;Shengheyuan&rdquo; community (SHY), and a commercial residential quarter-&ldquo;Wanfenghuicheng&rdquo; community (WFHC) in the high-tech zone of Zhengzhou City, Henan Province. We used the social capital theory to analyze the interviewees&#39; record.<br />\n\t<br />\n\t<b>Findings</b>: In urban communities, social capital is the most important factor for children (aged 6 to 12) in their ICT use. Our findings indicate that children in families with higher levels of social capital, such as internal resources, family income, parent educational backgrounds and parents&#39; social network, have more-highly developed ICT skills. Personal motivation and obstacles, such as lack of access to computers on a regular basis, also have an impact on children&#39;s ICT use. External social capital, including schools, libraries, and public service institutes, have little impact on children&#39;s ICT use, if not combined with internal social capital factors.<br />\n\t<br />\n\t<b>Research limitations</b>: Our research samples were collected from two communities within the same city, which may influence the generalization of this research result.<br />\n\t<br />\n\t<b>Originality/value</b>: To explore the social capital&#39;s influence on children&#39;s ICT use, we used field observation for ICT use of children aged 6 to 12 in urban communities in China, and studied the children&#39;s ICT behavior from the perspective of internal and external social capital.</div>
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".