Minor language, major challenges: the results of a survey into the IT competences of Finnish translators
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
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.
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.
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 | 0.010 | 0.001 |
| 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 | 0.001 | 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 it