Properties of Teacher Networks in Twitter: Are They Related to Community-Based Peer Production?
Bibliographic record
Abstract
Teachers participate in social networking sites to share knowledge and collaborate with other teachers to create education-related content. In this study we selected several communities in order to better understand the networks that these participants establish in Twitter and the role that the social network plays in their activity within the community, especially related with peer production. We analyzed the topology of these networks in two ways: a) the indirect relations by counting followers and followed people; and b) the conversational networks by counting mentions in tweets. We also analyzed the communities’ websites in order to elucidate whether their production was lightweight or heavyweight peer production. Results indicate that teacher networks adopt a community clusters archetype in which some teachers act as bridges between several groups. Although these networks do not form a tight crowd, their degree of tightness is superior to that of the general networks established in Twitter. Our results also indicate that the degree of tightness is important for sustaining heavyweight peer production and strong leadership can play a crucial role in establishing long-term commitment to a collective task.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".