Italiano e dialetto fra percezione e produzione nei bambini
Bibliographic record
Abstract
Recent studies have shown that bilingualism might influence cognitive functions other than language. The goal of this study is to detect if these advantages are also for those bilinguals speaking Italian, (national language) and dialect (local language). The research project examines developmental patterns in the narrative skills of Italian children. The research investigates a longitudinal sample of 22 children speaking only Italian (I), and 26 children speaking Italian and dialect (ID). In the first session children are in Grade 1 of two Primary schools in southern Italy; the second session, set one year later, use the same procedure of the first session, but there is an additional questionnaire about the perception of the two languages by the children. The results of this questionnaire show that there are still misconceptions about language diversity: dialect is still considered a stigma. Data are gathered by using a narrative assessment tool: the child tells a story created by ENNI (Edmonton Narrative Norms Instrument), which involves story formulation from visual material only. Afterwards, children participate in comprehension task involving the same pictures to assess Story Grammar knowledge, Complexity Index, First mentions. Results show that ID’s performances are slightly better than those of I’s ones. A part from the presence of more colloquialisms and regionalisms in ID’s productions, the results of both groups are the same:the cognitive level of the children don’t differ regardless the language used. In both groups’ productions is evident the failure of bilingualism of Italian and dialect: the languages used are semi-languages, mixed, lacking their own independence. Italian and dialect seem to oppose and phagocitise each other provoking the failure of bilingualism. Children have learnt a language already mixed instead of two different languages. Dialects have always been considered as not-languages, instead of being preserved, provoking the slow loss of dialects.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".