Who Does This Language Belong To? Personal Narratives of Language Claim and Identity
Bibliographic record
Abstract
In this Hebrew language learning setting, students’ backgrounds and histories are diverse: some were born and raised in Canada, the United States, or South Africa and studied Hebrew at Jewish day schools; others were born in the former USSR, immigrated to Israel as children, and moved to Canada with their families as teenagers; others were children of Israeli emigrants who learned Hebrew at home. This ethnographic qualitative study examines two conflicting camps within the Hebrew class, defined by themselves and Othered by opposing sub-groups as “Canadians” and “Israelis”. As the students and the author negotiate their strong ties to the language with Othering and exclusion by other sub-groups from the dominant speech community, the sentiment of the Israeli emigrant professor regarding her students hangs overhead: “None of them are Israelis. None of them are native speakers of Hebrew.” Who does this language belong to? Which subgroup can declare authenticity as real, rightful owners of the language and its indelible culture and identity?As language programs worldwide deal with a diverse and heterogeneous student population who enter the classroom categorized as heritage, second, bilingual, foreign, or native language speakers, this book addresses clashing and Othering between sub-groups over the authenticity of the variety of the language and its speakers, and who can rightfully claim the language as their own.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.038 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.009 |
| 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".