Assessment and recommendations
Bibliographic record
Abstract
The share of 25-64 year-olds in Portugal who had completed at least upper secondary education increased from 20% in 1992 to 47% in 2016; for those aged 20-24, 78% had completed at least upper secondary in 2016. Furthermore, 15-year-old students in Portugal saw the greatest improvements in their science abilities of any OECD country as measured by the OECD Programme for International Student Assessment (PISA) between 2006 and 2015. The average score in science increased from 474 in 2006 to 501 in 2015; simultaneously the proportion of 15-year-old students scoring below Level 2 (below baseline proficiency) declined from 24.5% to 17.4%. These improvements in students’ scientific skills were accompanied by similar substantial improvements in 15‑year-olds’ reading and mathematics skills, trailing only one OECD country in their improvement rate. Though not as consistently, younger Portuguese students have also demonstrated improvements in their abilities. While Portuguese students in their fourth year of primary instruction have shown strong improvements in their mathematics skills over the past 20 years on the Trends in International Mathematics and Science Study (TIMSS), fourth year primary students have shown uneven patterns of gains and losses in their reading skills on the Progress in International Reading Literacy Study (PIRLS). Nevertheless, a large proportion (13%) of Portuguese students continue to leave school before completing secondary education and fail to secure a job or continue their education, repetition rates remain almost 3 times the OECD average (34% vs. 12%), and between one-fifth and one-quarter of Portuguese 15-year-olds lack baseline skills in mathematics, reading or science.
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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 | 0.027 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.263 | 0.160 |
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".