Bibliographic record
Abstract
As the world moves to a post-COVID stage and movement of goods and people across borders resumes, we need to rethink how we communicate and educate students about communication in a superdiverse world with increased presence of minoritized languages and varieties. The growing evidence of translanguaging practices among plurilingual speakers in multilingual societies and linguistic minority communities across the globe (e.g., Cenoz & Gorter, 2017; Oliver et al., 2020; Seals & Olsen-Reeder, 2020; Straszer et al., 2022) has prompted greater attention to equity and linguistic social justice issues in language education. Pedagogical translanguaging has been put forward as an “all encompassing” (Li, 2018, p. 9) practice to address linguistic inequities and injustices in the classroom. While it is a step forward in countering monolingual ideology and the dominant-language-exclusive policy and sanction, I draw attention to the “selective” nature of much of the current pedagogical translanguaging approach and argue for “inclusive translanguaging” that capitalizes on all of the languages, cultures, and identities of plurilingual speakers who have historically received marginalization, including their non-dominant dialects or mother tongues.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.033 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".