The Status of Professional Business Translators on the Danish Market: A Comparative Study of Company, Agency and Freelance Translators
Bibliographic record
Abstract
This article reports on an investigation which forms part of a comprehensive empirical project aimed at investigating the status of professional translators and interpreters in a variety of contexts. The purpose of the research reported on here was to investigate the differences in terms of occupational status between the three groups of professional business translators which we were able to identify in relatively large numbers on the Danish translation market: company, agency and freelance translators. The method involves data from questionnaires completed by a total of 244 translators belonging to one of the three groups. The translators’ perceptions of their occupational status were examined and compared through their responses to questions evolving around four parameters of occupational prestige: (1) salary/income, (2) education/expertise, (3) visibility, and (4) power/influence. Our hypothesis was that company translators would come out at the top of the translator hierarchy, closely followed by agency translators, whereas freelancers would position themselves at the bottom. Although our findings largely confirm the hypothesis and lead to the identification of a number of differences between the three groups of translators in terms of occupational status, the analyses did in fact allow us to identify more similarities than differences. The analyses and results are discussed in detail, and avenues for further research are suggested.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".