Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article discusses the contributions of digital technology within the school environment in France by comparing numerous myths circulating in this field derived from research: increased motivation and autonomy of students, more active learning, a response to specific needs... does digital technology really enable all of this? What's teachers' attitude towards digital tools? It turns out that the answer to these questions is mixed, with education still far from having completed its "digital revolution". Digital tools indeed boost student motivation, but the impact is modest. Research suggests students may be overwhelmed, find it too demanding, or misuse interactive features. Digital technology's promise of autonomous learning is also challenged. Offering resources alone doesn't guarantee engagement, with attendance and motivation playing crucial roles. Digital tools benefit students with disabilities in various ways, but the effectiveness of some interventions remains moderate, highlighting the need for ongoing research and replication. When it comes to teachers, their use of digital tools varies from subject and type of task, and only when the use has added value.
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.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it