MétaCan
Menu
Back to cohort
Record W3210139130 · doi:10.26466/opus.957777

Comparison of Turkey and Countries with High PISA Achievement in terms of Education Expenditures and Academic Achievements

2021· article· en· W3210139130 on OpenAlexaboutno aff
İlknur Maya, Sedat YAKUT

Bibliographic record

VenueOpus uluslararası toplum araştırmaları dergisi · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceGross domestic productEconomic growthHigher educationGeographyPopulationEconomicsDemographySociology

Abstract

fetched live from OpenAlex

The aim of this research is to compare the education expenditures of the Economic Cooperation and Development Organization (OECD) countries such as Canada, Finland and South Korea, which are successful in the 2018 cycle of the Program for International Student Assessment (PISA) with Turkey, and to make suggestions for Turkey. The research was designed in the scanning model. The research population is 36 OECD countries. Canada, Finland and South Korea which were succeed in the 2018 PISA and Turkey were selected as samples. Data were obtained from OECD reports on the 2018 PISA cycle and other international and national documents. In the research, document analysis method was used. According to the results of the research, it has been seen that the education expenditures of Canada, Finland and South Korea are high and the countries are successful in PISA. Therefore, it can be said that there is a positive relationship between PISA achievement and education expenditures. Public education expenditures only in higher education in Turkey are higher than Canada, Finland and South Korea and the OECD average. Education expenditures per student in Turkey are below the OECD average and almost a third of that of Canada, Finland and South Korea. There are significant differences when comparing Turkey with other countries (Canada, Finland and South Korea) in terms of Gross Domestic Product (GDP), expenditure per student and teachers' salaries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.397
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueOpus uluslararası toplum araştırmaları dergisiSame topicEducational Assessment and PedagogyFrench-language works237,207