“Juggling while running”: Emergency remote teaching of translation in times of educational disruption
Bibliographic record
Abstract
The COVID-19 pandemic caused an unprecedented educational disruption in university programs worldwide, with translator education being no exception. The sudden and unplanned transition from campus or blended to online instructional environments, termed “emergency remote teaching” or ERT (Hodges, Moore, et al. 2020), imposed a unique strain on students, teachers and institutions. Aiming to reassess the concept of ERT so as to enable a better response in the future, this study investigates the reflections of nine translation teachers from three universities in Croatia on their ERT experience in the March-June semester of 2020. To this end, three semi-structured focus groups were conducted in July 2020. Results show that the teachers had to adapt to the new learning environment and cater to their students’ changed learning and emotional needs, while reorganising their home life and learning new skills. In these circumstances, described by one of the participants as “juggling while running,” the social support given and received by teachers was found to be a crucial factor at play. The experience is shown to have had an impact not only on their ERT, but also on their future practices. Some recommendations are drawn in the conclusion.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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; 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".