Studying school communities as multiplex dynamic networks: The “RECENS Wired into Each Other” Dataset, 2010–2013
Bibliographic record
Abstract
Abstract This article provides an overview of the “Research Center for Educational and Network Studies (RECENS) Wired into each other” longitudinal social network study conducted in 44 secondary-school classrooms in Hungary between 2010 and 2013. Participants were asked to fill out paper-based surveys four times over a 3-year period ( n = 1,767 students). These surveys explored peer relations and perceptions within each classroom in over 30 distinct network dimensions, including shared social activities, ties of affection, bullying and victimization, perceptions about peers’ traits (including their ethnicity), behaviors, social roles, and status. Alongside the network data, we collected information about students’ individual background (e.g., sex, ethnicity, socioeconomic status) and behaviors (e.g., smoking, studying habits). We further interviewed the main teacher of each classroom to gather data about teaching arrangements and teacher perceptions of students. The current article aims to provide context for the dataset and documentation available online (Vörös et al., 2022). The dataset has been used in several published research articles, and PhD and MA theses in recent years. However, we believe that its publication is still highly relevant, as various measures in the rich dataset remain unexplored to date. After describing the study and available data, we review the main topics the study team and our colleagues have explored in recent years. We then outline a few promising directions for further inquiry into the data, which could all leverage the unique multiplex information gathered in the surveyed communities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".