Investigating Translators’ Work-related Happiness: Slovak Sworn and Institutional Translators as a Case in Point
Bibliographic record
Abstract
This paper reports on an investigation which is part of a comprehensive project aimed at investigating translators’ work-related happiness in various contexts, a subject largely under-researched in contemporary translation studies. The purpose of this pilot cohort study is to determine the perceptions of happiness in two presumably high-profile groups of translators—Slovak sworn translators and Slovak EU translators. To accomplish the aim, comparative and causal perspectives are used. The quantitative analysis, comprising descriptive and correlation analysis, involves data from questionnaires completed by a total of 115 translators belonging to the two groups (83 + 32). The respondents’ perceptions of their work-related happiness are examined and compared, based on their responses to questions revolving primarily around social status variables and parameters of occupational prestige. Based on the gained data, seven hypotheses are tested with quantitative research methods employing contingency tables. Although our findings largely do not corroborate the hypotheses and lead to the identification of crucial differences between the two groups, the analyses also allow us to identify some commonalities. The results of the quantitative analysis are discussed in detail.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".