The Impact of ChatGPT on Part-Time Translators Working with the English Language: A Threat or a Complementary Tool?
Bibliographic record
Abstract
Since its launch on November 30, 2022, the artificial intelligence (AI) language model ChatGPT has garnered immense attention and experienced exponential growth in popularity. As one of the largest and most sophisticated models in the market, ChatGPT presents both challenges and opportunities for part-time translators, especially those working primarily with the English language. However, there exists a pervasive perception that part-time translators may face unemployment due to AI advancements. This study examines the attitudes of part-time translators toward the fear that ChatGPT will replace them and explores strategies for adapting to the AI-driven environment. Using an empirical sociological approach with mixed methods, this research finds that part-time translators view ChatGPT as a tool that can augment their capabilities and enhance job efficiency when translating into or from English. The findings suggest that AI can serve as a complement rather than a replacement, providing valuable support for less creative or repetitive translation tasks. This study provides a foundation for future research on the impact of AI on vulnerable occupations within the English language sector.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".