Bibliographic record
Abstract
The history of Translation Studies in Finland is still largely unstudied. Particularly little research has so far been done on scholarly publishing. The well-known exception to the rule is Gideon Toury (2009), who when studying the statistics of the prestigious journal Target from its first twenty years (1989–2008), observed that Finland was among the major contributing countries and thus a central country on the map of Translation Studies. This paper investigates what happened to Finland’s centrality in Target during 2009–2020, albeit with different methods. Furthermore, the analysis has been extended to five other well-known journals: Meta, Perspectives, The Translator, trans-kom and Translation Studies, in order to study Finland’s visibility in Translation Studies on a larger scale. The analysis shows changes in the proportions of contributing countries in Target. Scholars with a Finnish affiliation represented only 3 percent of all (co-)authors 2009–2020. However, when multiple appearances are calculated the proportions are slightly different, and Finland ranks ninth with Canada among the ten most contributing countries to Target. As to the other journals, Finnish scholars have a visible position (8 per cent of all authors) only in trans-kom, whereas in the four other journals, they represent 1–3 per cent of all contributors.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.014 |
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".