Lost in Translation: Problems of Rendering the Term Sustainable Development into Non-Western Languages As Demonstrated in the Case of South Korea
Bibliographic record
Abstract
This study seeks to demonstrate the usefulness of a relatively underutilized approach to studying sustainable development as a term and concept. While studies on sustainable development have generally followed a normative approach seeking what the term should ideally mean, this study follows a historical approach such as recommended by the historian of philosopher Quentin Skinner to explore what changes of definition it has been capable of undergoing in the actual use. To illustrate why such changes may be a critical issue, we have deliberately focused on the case of a country – South Korea – where the very translation of the term into the native language, combined with other factors, has resulted in sustainable development being generally understood by the public as meaning something quite different from the more normative understanding of the term. Instead of a balanced development that protects the environment and promotes social welfare as well as promoting economic growth, sustainable development in the standard Korean translation has come to be understood as simply meaning continued economic growth, which is to be sought even at the expense of environmental degradation. For documentation and analysis, we have relied on various methods, while focusing on key sectors and select policy areas, including energy. We conclude with further reflections on why an approach such as ours might be a useful methodological addition in sustainable development 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 | 0.036 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".