Minor language, major challenges: the results of a survey into the IT competences of Finnish translators
Bibliographic record
Abstract
This article discusses the IT skills of Finnish translators. It presents the results of an online survey, conducted from December 2012 to May 2013. The total number of responses was 238, and the respondents are graduates of various universities who work with various language pairs (with Finnish as language A) and specialise in different fields. One quarter of the respondents are male, and more than half represent the younger generation (>36 years of age). The respondents' evaluation of their IT skills shows a satisfactory level of competence. Most of the respondents are competent at text processing and performing Internet searches, and the majority have some skills in computer maintenance. Many respondents are not very familiar with CAT tools, although some are active users of this software. However, most translators have little or no experience of image processing, hypertext markup, or spreadsheet software. Results show that the respondents are critical of the training in translation technologies they received at university. They also evidence Finnish translators' belief in IT skills as vital to contemporary translation work.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".