Bibliographic record
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?" Compounding the matter was the BBC's inclusion of statements by Prince Charles on computers and learning. "I simply do not believe that passion for subject or skill, combined with inspiring teaching, can be replaced by computer-driven modules, which seem to occupy a disproportionate amount of current practice." While computers stole the headline, the Woessmann and Fuchs report contained numerous other bombshells for the educational sector. Small class sizes have no impact on educational outcome, they argued. Private schools have a positive impact. So do standardized exams. Additionally, school autonomy (hiring of teachers, textbook choice and budget allocations) is related to superior student performance. And students in public schools perform worse than students in private schools. Better equipment with instructional material and bettereducated teachers also improve student performance.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".