School University Consortium to Enhance General Certificate Student’s Prospective and Academic Performance in Palestine
Bibliographic record
Abstract
This paper introduces a conceptual platform for bringing students and teachers together in a social media consortium. The results extracted from the questionnaire used in this paper exhibits that the majority of the students support and are eager to see this idea live and willing to play an active role and show full commitment. This consortium encompasses students and teachers from both school and university. This platform prepares the students, fosters and enables them to a smooth transition from school to university, as well as improving the students’ communication skills and academic performance by using mentoring, tutoring and coaching techniques. As a case study of social media, Facebook was used as a communication and interactive tool amongst group members. The theme behind this platform is to construct academic group from final year school students with first year university students to exchange experience and transfer knowledge. This group has school teachers as well as university teachers. Each group has a mentor, coach and tutor. Each of them will play a specific role throughout the group, which will be highlighted in this paper. The outcomes were useful and interesting for students, their parents and teachers involved. It was a great experiment and recommended to widen it to involve more students and teachers.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".