Analysis of Gender Differences in Internet Use of Children: Evidence from Turkey
Bibliographic record
Abstract
In this study, factors in Internet use of female and male children in Turkey were determined with probit regression model by using micro data set in Household Information Technologies Usage Research of 2013 carried out by Turkish Statistical Institute. Dependent variable of the study is two category variable, namely Internet use and non-use of female and male children. Independent variables are socio-economic and demographic variables. According to chi-square analysis, there is a relation between Internet use of female and male children and socio-economic and demographic characteristics. According to probit regression analysis results, for female children, region, educational status, having computer or mobile phone on their own, frequency of watching TV, watching movie, series; floor show, music, game show; watching educational programs such as documentaries, culture, art, reading newspaper and journal in printed media, using mobile phone and frequency of using computer are variables effective in Internet use. Region, rural-urban difference, age, being literate, educational status, having mobile phone or game console on their own, watching entertainment, music, competition programs, reading newspaper and journal in printed media, using mobile phone and, frequency of using computer are variables effective in Internet use among male children. Frequency of using computer is the most effective variable in Internet use and it is more effective among female children compared to male children.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".