Reimagining Linguistic Heritage: Or How Mother Tongue Speakers Re‐Create Their Language
Bibliographic record
Abstract
In this article, I engage the “language as heritage” trope to critically examine a popular belief that underlies it: the idea that a shared language primordially connects an individual to a group of people, a homogenous culture and a particular territory—the notion of the ethnolinguistic group. Judith T. Irvine has long urged linguistic anthropologists to problematize the linguistic side of these classifications, to recognize the ideologies that shape both scholarly language descriptions and speakers’ own interactional practices (often in response to those official depictions). Here, I take on this challenge by considering both the contrived colonial standardization process of East Africa’s Swahili language, and contemporary Swahili speakers’ creative resistances to scholarly descriptions of “their” linguistic heritage. Orthographic and interactional practices from speakers of KiAmu, a Swahili vernacular spoken in coastal Kenya, illustrate how speakers creatively attempt to make their vernacular more “like itself.” Rejecting (post)colonial perspectives of Swahili as a distinctly “African” language, they are reimagining their linguistic heritage and its associated belongings to appeal to alternative identities and histories, that have hybridity and transoceanic interconnectivity at their core.
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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 | 0.004 | 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 | 0.006 | 0.022 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".