What Online Networks Offer: Online Network Compositions and Online Learning Experiences of Three Ethnic Groups
Bibliographic record
Abstract
This exploratory study examines ethno-cultural diversity in youth´s narratives regarding their online learning experiences while also investigating how these narratives can be understood from the analysis of their online network structure and composition. Based on ego-network data of 79 respondents this study compared the characteristics of the online social networks of native Dutch, Moroccan-Dutch, and Turkish-Dutch youth. Subsequently, thirty interviews were analyzed to compare youth’s narratives regarding two aspects typically associated with 21th century online learning: ‘ individual online exploration’ , and ‘ participation, collaboration and exchange of information in online communities’ . The results show that the three ethnic groups significantly differ regarding their online network composition. Youth’s narratives also reveal that their online learning experiences are ethno-specific. Youth differ regarding the nature of online communities in which they search for information, make new contacts and distribute their own media creations. For example, Turkish-Dutch youth primarily engage in their own ethnic transnational networks to find information and to share media content, whereas Moroccan-Dutch youth seem more open to develop new contacts and to search for information outside of their familiar network. It is suggested that these ethno-specific narratives can be understood as resonating specific network configurations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".