The Role of Classroom Seating Arrangements in Friendship Formation and Persistence
Bibliographic record
Abstract
Abstract This research study aims to analyze the differences existing between teacher-assigned seating and free seating in classrooms in terms of their impact on students’ friendship formation across a semester. The goal is to bridge the literature on seating arrangements, spatial propinquity, and friendship networks for teenagers. Through a social network analysis of friendships in a classroom at the beginning and end of a semester in addition to the seating maps at play, it is possible to test how different seating influences adolescent social structure. This innovative way of tackling spatial propinquity in schools not only has the potential to improve our understanding of high school friendship structures and how they relate to classrooms but also to increase our knowledge of the effects of seating assignments in high schools, a practice that is widespread in the United States. This study analyzes 410 student respondents in 24 American high school classrooms longitudinally across a semester. The results were unexpected: when students choose their seats, the formation of friendships tends to be more influenced by seating proximity, and these friendships tend to be less racially homophilous than when teachers choose seats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".