“Language Lives in Our Bodies Not Just in Our Heads”: Embodied Reading and Becoming Beyond the Molar
Bibliographic record
Abstract
This article focuses on one aspect of a literacy research project: how reading and language enable embodied processes that allow for fluidity and becomings outside of the static, molar normative discourse in society and consequently in language education. I explain how one research participant continues becoming outside of white settler-colonial understandings of bilingual-immigrant-racialized-woman, through reading a counternarrative fiction in a book club. Using a feminist Deleuzian methodology, I blend different data to make connections drawing on Coloma, Deleuze and Guattari, and Sumara. Through the analysis of one hot spot, I explain how the participant continues becoming through her self-identification as a speaker of Spanish and English, Venezuelan, Latinx immigrant-settler woman, in ways that resist molar, binary white settler-colonial understandings of her subject positions within education and literature, and how she creates a more liveable life through molecularity or fluidity. The inclusion of counternarrative fiction is pertinent for language classrooms, as creating a more liveable life beyond white settler-colonial binaries through embodied processes of reading fiction creates many possibilities for minoritized students.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".