Bibliographic record
Abstract
Understanding PISA Stephen DOWNESMoncton, CANADA ABSTRACT The headline was dramatic enough to cause a ripple in the reading public. "Students who use computers a lot at school have worse maths and reading performance," noted the BBC news article, citing a 2004 study by Ludger Woessmann and Thomas Fuchs (Fuchs and Woessman, 2004). It was not long before the blogosphere took notice. Taking the theme and running with it, Alice and Bill ask, "Computers Make School Kids Dumber?" They theorize, "If you track the admitted decline of education, you'll probably notice that it follows along with the increase of technology in the classroom." In a similar vein, James Bartholomew asks, "Do you think that the government will turn down the volume of its boasting about how it has spent billions introducing computers in schools (while keeping down the pay of teachers so much that there are shortages)? Do you think it will stop sending governors of state schools glossy pamphlets about insisting that computers are used in their schools as much as possible?" In this study, therefore, PISA looks well beyond educational attainment, and also includes school demographics, such as whether it is a public or private school, has large or small classes, or has access or not to technological resources. Finally, it does measure student information-their family background, access to books and computers and parental support as well. The PISA survey departs from previous surveys in disregarding the stated curricula of the schools being measured. Therefore, the conclusion is not surprising, nor even wrong for him to consider independently of any parental or teacher support, considered without reference to the software running on it, considered without reference to student attitudes and interests, does not positively impact an education. Finally, he focus on missing the reporting of results
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.039 | 0.010 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.095 | 0.016 |
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".