“If He/She Had Been Like the Rest of Us”. How Do Young People Describe Their Schoolmates Who Are Different from Others in the Group?
Bibliographic record
Abstract
The article examines issues related to peer interactions and group joining in upper secondary schools in Finland. The study elaborates on how young people describe students who are left out/excluded or who remain outside the social networks. The study also elucidates on how a student can join the group. The research is motivated by the current educational ethos, which emphasizes inclusion and tolerance. The data were collected from an upper secondary school and vocational and technical institute. The students were asked to recall the prior high school year and write an essay on the topic. A total of 49 students wrote about their memories. The data were analyzed using inductive content analysis, and the study found that students are either excluded or included due to the social skills they possess. Those who do not exhibit the same approach to being in a group will stay on the sidelines. The essays also described factors that connect students, such as hobbies and leisure activities. Similarity in many external factors (e.g., the family’s economic situation) unites students. Contrary to expectations, young people described themselves, and not just others, as outsiders.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".