“That Sounds So Cooool”: Entanglements of Children, Digital Tools, and Literacy Practices
Bibliographic record
Abstract
Many observers have argued that minority language speakers often have difficulty with school‐based literacy and that the poorer school achievement of such learners occurs at least partly as a result of these difficulties. At the same time, many have argued for a recognition of the multiple literacies required for citizens in a 21st century world. In this study the researchers examined a specific case in which English language learners (ELLs) made short videos about sustainability and social justice, to determine the diverse literacy practices such activities entailed. The researchers found that children produced storyboards and scripts, and videos with titles, and engaged in several other literacy activities, discussing what “made sense” in sequencing in a documentary story, what sustainability and social justice meant, how to report on information they had gathered, and so on. They also examined how new materiality theories might assist us in analyzing howELLs engage in digital literacy activities. These theories encourage us to think about how human beings interact with other kinds of materials to accomplish perhaps novel tasks. With respect to language learning, such a view might challenge our conceptions of language and literacy learning. For new materiality theorists, language and literacy cannot be an “out‐there” kind of “thing” that learners put “inside” themselves. Rather, languages and literacies and people and their activities and other materials accompany one another, and are entangled in sociomaterial assemblages that rub up against one another in complex and as yet unpredictable ways.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".