Socio-material assemblages: (De)colonizing literacy curriculum in transnational education
Bibliographic record
Abstract
Recent decades have witnessed rapid growth of K-12 transnational education programs, but little is known about how human/nonhuman assemblages impact K-12 transnational literacy curricula and how sociomaterial assemblages affect (de)colonizing literacy practices. This study of English and Mandarin literacy curricula at a Canadian transnational education program in postcolonial Hong Kong was informed by posthumanism and theories on decolonizing curriculum. The study combined ethnographic data collection tools (curriculum documents, interviews, classroom observations) and a diffractive methodology of reading, thinking, and writing with multiple data sources and theories to explore how sociomaterial relations between humans and nonhumans shaped the (de)colonization of literacy curricula. Findings show a generative sociomaterial assemblage in the transnational education program that enabled encounters of local-global curricula, local-global languages, and academic-multimedia literacies. New forms of imperialism and colonialism also joined the assemblage and normalized binaries of L1/L2, local/global, and academic/multimedia literacies, thus constraining students’ meaning making across languages, places, and semiotic resources. The article proposes literacy curriculum and pedagogies that could foster students’ ethical relationship building with humans and nonhumans in globalized schooling contexts.
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 | 0.002 | 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 | 0.008 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".