Applications for Raising Academic Performance in Terms of Equal Opportunities in Education in Countries with High Achievement in PISA
Bibliographic record
Abstract
This paper aims to analyse the applications for raising students' and schools' academic performance in the context of equal opportunities in education in such countries as Canada, Finland and Singapore with high achievement in PISA (Programme for International Student Assessment) in 2018 and thus to make recommendations to our country- Türkiye. A qualitative study method was used in this current study. Document analysis technique, a method of qualitative study, was used in collecting the data. The data collected were then described under certain headings through descriptive analysis. The applications for raising academic performance in the context of equal opportunities in education in certain countries with high achievement in PISA (2018) were analysed under the headings of 'applications of academic support for students in the context of equal opportunities in education in the countries' and 'applications of academic support for schools in the context of equal opportunities in education in the countries'. It was found in consequence that mostly local or school-based applications were available for students and schools in the countries. It is evident that applications are performed within a system. Interventions are made according to feedback. All the stakeholders take on active roles in the process. More systematic and local configuration involving all the stakeholders is needed in our country.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".