تقويم التعلم الخدمي بجامعة ولاية ميتشيجان وجامعة البرتا وامکانية الافادة منه في جامعة الزقازيق (دراســـــة مقارنـــــة)
Bibliographic record
Abstract
تهدف الدراسة بصورة أساسية إلى وضع اجراءات مقترحة للإستفادة منها بجامعة الزقازيق من خلال خبرة جامعة ولاية ميتشيجان وجامعة البرتا فى مجال تقويم التعلم الخدمى. وفى سبيل تحقيق هذا الهدف سارت الدراسة فى مجموعة من الخطوات، بدأت بالإطار العام للدراسة، ثم الأسس النظرية لتقويم التعلم الخدمى، تلا ذلک عرض خبرة جامعة ولاية ميتشيجان وجامعة البرتا، ثم تحليل واقع تقويم التعلم الخدمى بجامعة الزقازيق، وتضمنت الخطوة الأخيرة الاجراءات المقترحة التى يمکن من خلالها تقويم التعلم الخدمى فى جامعة الزقازيق فى ضوء خبرة جامعة ولاية ميتشيجان وجامعة البرتا فى هذا المجال. The main objective of this study is to develop suggested procedures for the benefit of Zagazig University through the experience of Michigan State University and Alberta University in the field of service learning evaluation, In order to achieve this goal, the study proceeded in a series of steps, starting with the general framework of the study, then the theoretical foundations of evaluating service learning, followed by a presentation of the experience of Michigan State University and the University of Alberta, then analyzing the reality of evaluation of service learning at Zagazig University. Through the evaluation of service learning in Zagazig University in the light of the experience of the University of Michigan and the University of Alberta in this fild.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.175 | 0.149 |
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".