Linguistic Challenges of Fieldwork for First-Generation Ethnic Researchers
Bibliographic record
Abstract
Language is a significant part of any fieldwork in cultural and cross-cultural qualitative research. With a growing number of 1.25-, 1.5- and second-generation children of immigrant families entering academia, many assume their bilingual proficiency helps them bypass translational and linguistic challenges. Based on my fieldwork experience, I am exploring the linguistic challenges of intragroup research. While I presumed my bilingualism in English and Farsi would help me build communal rapport during my fieldwork, I was surprised by my participants’ choice of broken English and refusal to complete their interview in Farsi. In this article, I argue that my positionality as a researcher and the intruding Anglo culture in the research process diminished my communal identity and intensified my hierarchical position. Upon assessing the linguistic challenges, I will make recommendations and suggestions to address similar challenges in the fieldwork.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.115 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".