Conversation with an Interpreter: Considerations for Cross-Language, Cross-Cultural Peacebuilding Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it