Socio-Family Context and Its Influence on Students’ PISA Reading Performance Scores: Evidence from Three Countries in Three Continents
Bibliographic record
Abstract
This investigation set out to analyse the relation between parents’ academic qualifications, profession and role in educating their children and their children’s level of efficacy in reading at the end of the adolescent stage, in three states with different socio-cultural contexts, namely Canada, Finland and Singapore. The study is carried out in three countries with differing socio-cultural contexts and uses multilevel analysis and binary logistic regression to measure the predictive value of socio-family skills in these three countries against a range of student reading ability profiles. The results show that the parents’ academic qualifications, profession and educational role are the most influential aspect of the predictability in the variability of their children’s reading skills. Parents with a low level of education predict poor student reading ability, but when it is the mother who has a medium or high level of education, the results of the students are better than when that level is only achieved by the father. Therefore, the educational role of mothers and fathers, as shown by the interest they take in their children’s schoolwork, is a predictor of students’ reading skills, regardless of the sociocultural and academic context of the students.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".