‘We’re not ethnic, we’re Irish!’: Oral histories and the discursive construction of immigrant identity
Bibliographic record
Abstract
This article examines how national and immigrant identities are discursively constructed through the use of oral histories, using a corpus of 15 oral-history interviews (25 hours of transcribed talk) collected from members of the Irish Association of Manitoba. Using a simplified discourse-historical approach, the analysis focuses on content, constructive strategies of assimilation and dissimilation, and the linguistic means by which those strategies are achieved, using Wodak et al.’s (1999) framework from an in-depth study of Austrian discourse and identity. While analysis of participants’ discourse about identity echoed much of the current theoretical knowledge available about identity — that it is a discursive construction revealed in narratives, that it is provisional and negotiated with others — the analysis also showed that for specific subgroups such as immigrants, identity construction is context-dependent, particularly for diasporic groups.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".