Translating Answers to Open-ended Questions in Multi-lingual Surveys. A Case Study of the Cross-national Longitudinal Study: Older Audiences in the Digital Media Environment
Bibliographic record
Abstract
This thesis looks into the process of translating answers in multi-lingual longitudinal surveys. It provides a literature review on translation in similar studies, but more importantly, through qualitative interviews with researchers involved in an international longitudinal study, it explores concrete strategies and challenges. The Cross-National Longitudinal Study: Older Audiences in the Digital Media Environment is investigated as a case study. The main working language in the study is English and the used questionnaire includes closed- and open-ended questions. Dealing with respondents’ answers includes translating answers to English from many languages. Researchers in this study do not pay attention to translating answers to great extent, and some think open-ended questions are not necessary in quantitative research. Those who translated answers had problems with language comprehension and cultural backgrounds. They lacked certain guidelines which would help with solving any issues. This thesis identifies the challenges that researchers face and finds possible strategies, and improvements for translations.
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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 | 0.056 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".