Forecasting the Translation Profession Development: Foresight Technology
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 article describes the results of the conducted research based on the foresight technology that is widely used for long-term forecasting, representing a way of building a coherent and balanced image of the future. It was the first Foresight session that was devoted to the translator’s profession in Russia. The participants tried to predict the future trends, technologies, possibilities and risks of the translator’s profession in the future (up to 2030). The session was not limited to common forecasting: the task was not only to imagine the future of the translation profession but also to suggest the actions needed to achieve positive results, i.e., participants not only designed the future but also considered possible ways of developing and stabilizing the translator’s profession.The analysis of different foresight sessions in the field of education that were held in Russia, European Union, Great Britain, Canada, and the USA are also presented in the article.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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