SCRIVERE E RISCRIVERE I TESTI IN CHIAVE ACCESSIBILE E RAPPRESENTATIVA: ALCUNE INDICAZIONI PRATICHE
Bibliographic record
Abstract
L’obiettivo di questo lavoro è individuare un punto di intersezione tra gli obiettivi dell’educazione linguistica (e in particolare della didattica testuale) e quelli dell’inclusione. In primo luogo si cerca di chiarire che cosa si intende in generale quando si parla di un testo accessibile e rappresentativo in àmbito didattico. In seguito si danno delle indicazioni di scrittura o riscrittura di un testo con finalità didattica, al fine di guidare chi scrive nel processo, variabile e graduale, verso l’accessibilità e la rappresentatività. Tali indicazioni sono pensate come spunti rivolti a chi, a vario titolo, produce quotidianamente materiali destinati alla didattica: docenti di tutte le discipline, docenti di sostegno, autori e autrici di sezioni di testi scolastici, creatori e creatrici di contenuti con finalità informativa e formativa. La trattazione delle caratteristiche su cui concentrarsi durante la stesura di un testo il più possibile inclusivo è suddivisa in tre parti: 1) aspetti materiali, grafici e di impaginazione del testo; 2) aspetti più specificamente linguistici; 3) aspetti legati alla rappresentatività. Infine si danno dei suggerimenti di attività da proporre in classe, nel tentativo di mostrare come la manipolazione guidata di un testo in senso inclusivo è un’operazione che in parte può coinvolgere anche le studentesse e gli studenti, permettendo loro di esercitarsi nell’uso della lingua e al tempo stesso di riflettere su di essa. Writing and rewriting texts in an accessible and representative way: some practical suggestions The purpose of this paper is to identify a meeting point between the objectives of language education and those of inclusion. First it is necessary to clarify what is meant by “an accessible and representative text” in a didactic context. Subsequently, suggestions are given for writing or rewriting a text for educational purposes, in order to guide the writer in the process towards accessibility and representativeness. These suggestions are for those who produce texts as didactic materials on a daily basis: teachers, authors of school textbooks, creators of contents for informative and training purposes. The characteristics to focus on when drafting an inclusive text are organized into three sections: 1) material, graphics and layout of the text; 2) linguistic aspects; 3) representative aspects. In conclusion, suggestions of activities are given for use in class, in an attempt to show how the guided manipulation of a text in an inclusive sense is an operation that can also involve students, allowing them to practice using language and at the same time to reflect on it.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".