L’uso delle mappe concettuali per lo studio delle dipendenze da internet: una Unità didattica
Bibliographic record
Abstract
The article provides an account of a research/Teaching unit that took place in a Classical High School in Rome. It began at the end of the I year and developed in the I quarter of the II year, divided into two phases, as part of the Civic Education lessons. The first purpose was to make students aware of the problems of Internet addiction, to make them self-conscious of the risks that they can run, especially following a period of pandemic, which has forced the entire Italian (and world) population to confinement at home. The second purpose was to test whether the use of concept maps was indeed useful in producing in students the desired knowledge about Internet Addiction Disorder. Concept maps were created first on research made independently by the students, then through a summary on the topic provided by the teacher. After each of the two steps, the same questionnaire was administered: the results were finally compared. It turned out that, in the transition from Step I to Step II, new learning had indeed occurred. The learning experience was fully satisfactory, and the pupils themselves confirmed that the concept maps helped them to organize and deepen the new knowledge they were acquiring.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".