Bibliographic record
Abstract
This chapter thinks about ways that language is lived interstitially – between registers, accents, national histories, and personal travels – as something that (embarrassingly) always spills out or crops up when one is least ready for it to do so, revealing or mis-revealing a particular linguistic genealogy. Looking closely at Québécois poet Michèle Lalonde’s iconic 1967 poem-manifesto Speak White, and various recorded performances of this poem by speakers offering distinctive manners of accenting or pronouncing the bilingual (English–French) relations and agonisms enacted in the poem, this chapter further reflects autoethnographically or autocritically at ways the author’s own transnational and hybrid relation to these languages further helps to complicate national and international narratives. At once personal and political, historical, and critical, the chapter reflects on ways that language performatively offers an affective archive of one’s embodied and ancestral trajectories, and fails ever quite to account for how we experience the migrations and misalignments of our everyday.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.009 |
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".