Reconceptualizing the Link Between Validity and Translation in Qualitative Research: Extending the Conversation Beyond Equivalence
Bibliographic record
Abstract
Qualitative researchers often take for granted that the process of translation involves finding in the target language an equivalent linguistic expression to the one used in the source language. The validity of translation in qualitative research is thus based on the equivalence between the original and the translated texts, and correspondingly, uncertainty and differences between the two are treated as threats to validity and trustworthiness. Integrating insights from critical translational theories and Phil Carspecken’s critical reconstructive analysis, we demonstrate that a series of possible meanings always co-exists in the interpretation of a single speech act in both an original text and its translation. These nuanced meanings carry both foregrounded and backgrounded historical, inter-, and intra-cultural references. Through the application of critical reconstructive analyses to original and translated texts, we use examples to demonstrate an approach to achieve reflexivity and criticality through embracing, dialoguing about, and reflecting upon the uncertainty and difference in the meaning-making process of translation. Under this new approach, equivalence is not the sole criterion to evaluate the validity and trustworthiness of translation-related work in qualitative research; uncertainty and difference are not merely threats to the validity of qualitative research. We argue that, if addressed appropriately, uncertainty and difference can catalyze researchers’ interrogation of their own positionality as well as various forms of power dynamics, and thus enhance the validity of qualitative research.
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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 | 0.589 | 0.586 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.025 | 0.187 |
| Scholarly communication | 0.032 | 0.065 |
| Open science | 0.008 | 0.046 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier 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".