Bibliographic record
Abstract
viewed as key factors in the development of society and as strategic factors in tomorrow’s global economy. The implementation and development of a scientific and technological culture rely, among other things, on different institutions such as schools. Actually, tomorrow’s adults are now starting or about to start their schooling process at elementary schools. This is why, whether in America or in Europe, technology is now part of educational programmes with varying degrees of inclusion. With this frame of reference in mind, I started investigating a specific aspect of technology in elementary schools- the use of calculators in mathematics education. Its inclusion in educational programmes is becoming more and more explicit. For example, in the US, more and more states are reviewing the training of teachers to include the use of calculators (Hembree & Dessart, 1992) whereas in Great Britain, the use of calculators is already part of the curriculum in elementary schools. In Ontario, there are four proposed areas of study from grade 1 to grade 9, one of which is Mathematics, Science and Technology. THE CONTEXT Calculators have been available on the market for more than twenty years. When they first appeared, they prompted a debate on their usefulness in mathematics education. Most teachers wanted to ban them from the school. The debate then was overshadowed by the outspring of computers which tooh the lead in 1. I would like to acknowledge the financial support of Texas Instruments in this research.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.659 | 0.387 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".