Fear of a Black Language: A Radical Language Policy Perspective to the Afrobarometer
Bibliographic record
Abstract
Aim/Purpose: This paper complicates Black English-speaking Quebecers by mapping them to the poli-tics of multilingualism, through attempting to join Black and African thinkers on the dialectics of linguistic (in)securities. In particular, it joins “Francophone” and “Anglo-phone” Caribbean intellectuals: Édouard Glissant and Edward Kamau Brathwaite, in dialogue with Ngũgĩ wa Thiong’o, a pan-African intellectual. Background: Official language policies, ideologies, and practices within Canada and Quebec contribute to the marginalization of the Black population, in particular Black English-speakers in Quebec, as a double minority. Language is an important intersectional factor effecting the wellbeing of Black English-speaking Quebecers as it plays a significant role in their “experiences” of racial discrimination. Methodology: To challenge various forms of hegemonies and obscuration of violence that positivist logics engender through quantitative means; this paper aligns with emerging approaches critical quantitative approaches. Binary logistic regression model based on linguistic variables (home language practice) and non-linguistic variables (country, age, agency, trust) is employed to predict absence or presence of linguistic security using SPSS. Findings: The results of the logistic analysis indicate that the five-predictor model provided a sta-tistically significant prediction of linguistic security, χ2(5, n = 3891) = 114.759, p < 0.001. This is points to how individual language policy operates in a “complex ecological rela-tionship among a wide range of linguistic and non-linguistic elements, variables, and fac-tors.” A critical implication of this is for language to be taken seriously, in newer rounds of the Afrobarometer, as it was in former years. This would entail challenging the nation-state framework of the Afrobarometer. Impact on Society: Language policies historically, and in present times, are tools of linguistic hegemony, colonial violence, and repression. Thinking about how language policy operates beyond nation-state borders with Caribbean and Pan- Africanist thinkers, in relation multilingual being, carves new possibilities for linguistic securities.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.026 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".