A posthumanist orientation and cross-national, online bilingual digital storytelling
Bibliographic record
Abstract
This cross-national, online biliteracy project connected 13 Canadian and Chinese bi/multilingual learners aged 11–15 years. The research was informed by posthuman conceptualizations of literacy, biliteracy, modes, and media, and disrupted binary thinking about mind/body, first language (L1) / second language (L2), and language/materiality in literacy education. A diffractive methodology facilitated exploration of how the dynamic relationship-building between humans and nonhuman entities shaped the bi/multilingual learners’ digital storytelling. The project intended to enable a generative assemblage that positioned languages, media, and places as interconnected, agentive repertoires. It supported bi/multilingual learners’ connection-making with various semiotic resources and their situated lifeworlds and opened opportunities for them to create new forms of meaning. Findings also show that the generative assemblage was affectively rewarding and enabled bi/multilingual learners to be fully engaged in their digital story creation and eager to try more. The article proposes a biliteracy orientation that nurtures ethical meaning-making across languages, modes, and media.
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 | 0.003 | 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 | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".