Conversation with an Interpreter: Considerations for Cross-Language, Cross-Cultural Peacebuilding Research
Bibliographic record
Abstract
The ongoing processes of peacebuilding involve dialogue (Lederach 1997) and co-discovery (Freire 1970), which can sometimes be facilitated through academy-initiated research. Qualitative research provides opportunities to move from a positivist approach to a more equal, participatory, interactive exploration that benefits all participants, including the researcher in a “co-production of knowledge” (Karnieli-Miller, Strier, and Pessach 2009 p. 279). Cross-cultural, cross-language research (where researchers and participants do not share the same language), with all its riches, brings particular challenges for all involved. Beyond the issues of power and perceived power in any kind of research (Sprague 2005), in cross-cultural and cross-language research, already complex interactions are both facilitated/navigated and multiplied with the addition of an interpreter (Wallin and Ahlstrom 2006) who becomes the conduit for all interactions. This article focuses on the experiences of a cross-language interpreter involved in a participatory action study in peacebuilding in her home country of Ukraine. Her insights on the role of the interpreter, and considerations for future studies are shared through a conversation with the primary/initial inquirer at the end of this qualitative mixed-method project.
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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 | 0.471 | 0.340 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.063 | 0.070 |
| Scholarly communication | 0.045 | 0.070 |
| Open science | 0.015 | 0.052 |
| Research integrity | 0.025 | 0.027 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".