Plurilittératies, pratiques textuelles plurilingues et appropriation: interrogations en didactique
Bibliographic record
Abstract
The contribution discusses translanguaging and learning through an observation of young children's textual practices during workshops in a science centre. The science centre adopted a plurilingual approach to teaching and learning to cater for the multiple needs of a diverse population of learners. The plurilingual posture anchors science education and literacy in the ecological complexity of the place, and fosters the multi-situated aspect of science knowledge. Within a perspective where knowledge is grounded in experience and movement (Ingold 2000), children's learning was contextualized through the use of tools and a play-based inquiry process, drawing on children's imagination, the aesthetic element of science and of technology, their full language repertoires and their bodies to develop more complex science understandings, multilingual awareness (Melo-Pfeifer 2015), and multilingual and multimodal literacies (Prasad 2018). The participants are 5-8 years old learners in a series of workshops and visits focussing on experiential science inquiry that engage children to experience and merge multilingual writing practices, art, and science learning. Multimodal data sources include child-initiated visual documentation, digital photographs, as well as researchers' field-notes and audio-video recordings of children's interactions around their textual practices.
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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".