Blogai mokyklą lankančių mokinių savęs vertinimo ir elgesio ypatumai
Bibliographic record
Abstract
Nonattendance, behavior problems and social disjuncture of young people shows that pupils have problems: such as the feeling of low self-assessment, lack of self confidence. Economical, social conditions, lack of activities cause different kinds of addiction, breaches of the law, offences among the teenagers. Thus, seeking to see the peculiarities of behavior and self-assessment of pupils who don‘t attend school, it is necessary to perform the detailed objective study by ascertaining which actions define different solutions of problems.\n\nThe study aim object - 5-10 grade secondary school children not attending school.\nThe aim - to analyse the peculiarities of behaviour and self-assessment of children having school attendancy problems.\nThe main goals:\n1.\tTo analyse the teenagers‘not attending school, behavior and self assessment problems, on the basis of scientific-pedagogical, psychological literature.\n2.\tTo analyse the attitude of teenagers having school attendancy problems towards themselves, relationship with teachers, parents and contemporaries.\n3.\tTo reveal the attitude of pupils, having school attendancy problems towards learning.\n4.\tTo prepare recommendations for school administrators and tearchers.\nTheoretical, empirical and mathematical statistic methods were used.\nTarget group: 302 5-10 grade students’ pupils, having attendancy problems from cities, small towns and villages of Lithuania and 302 tutors.\nConclusions:\nThe study show, that:\n•\ta quarter of students... [to full text]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.144 | 0.044 |
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".