Evaluation of Numeracy Skills of Adults According to the Results of PIAAC 2015 in Turkey
Bibliographic record
Abstract
In this research, it is aimed to put forward the variables that predict the numeracy skills in PIAAC of Turkey, to reveal the relationship between the numeracy skills with educational level, education level of parents, number of books in the household, and annual income. It is also aimed to evaluate the quality of educational outcomes of the Turkish National Education System (TNES). The research is designed in a descriptive-analytical method. The sampling of the study is 5199 households. Mean, t-test, correlation, and CHAID analysis were used in the analysis. In the study, the performance of the numeracy skills of adults in Turkey was found to be very low and, it was emphasized the quality problems of TNES have remained. Moreover, the most powerful predictor of numeracy skills is educational level. There is also a significant relationship between numeracy scores with education level, education level of parents, income, number of books, and computer use. It is expected to provide a new perspective to the policymakers and researchers in education about the appearance of the TNES.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".