Alignment of Translation Technology Training with Professional Practice in Mexico: A Glimpse into the Situation
Bibliographic record
Abstract
An exploratory study about the use of translation technologies in translation programs in Mexico reported that few professors teach technology in few translation courses. Some reasons for this were that they had not been well trained in their academic programs when they were students, or they lacked a more comprehensive knowledge of these technologies (Peña Aguilar 2018). Effective training was not possible for most of these instructors as students, and they seemed to be reproducing similar learning insufficiencies with future translators. Because of this, another survey-based project was devised to identify the use that professionals who graduated from Mexican translation programs are making of translation technologies. What could be their disposition towards the use of translation technologies? The results indicate that professional translators do not resort to the use of ‘core’ translation technologies very often, but do use other electronic resources useful for accomplishing their tasks. One in two translators thinks their income has increased due to their technology knowledge, and they learn about these technologies on their own. Additionally, they are partly enthusiastic and neutral about using translation tools of this type. Professional translators think they could have learned about translation environment tools (TEnTs) at university (and they wish they had), but university instructors are still not teaching these technologies as much. So, there is a need reported by a few professionals, but not being dealt by some university programs. This could tell about the need to change or revise translation programs or, at the very least, the need to have a change of attitude on the side of university instructors. In contrast, those that are doing their part would hopefully find some reasons to keep up.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".