The Indicative Power of A Key Word System. A Quantitative Analysis of the Key Words in the Translation Studies Bibliography
Bibliographic record
Abstract
Over the last decades, Translation Studies has explicitly tried to develop and regard itself as an interdiscipline. This evolution, as well as this self-esteem, has not only widened the focus of the field, it has also created a sometimes unclear eclecticism of topics, influences and methods. Characteristic of a still not too well-established and not always acknowledged discipline, research in Translation Studies has been looking for common interests and common grounds with other disciplines in an ambitious, but often unstructured way. The new online Translation Studies Bibliography (TSB - first release October 2004), which concentrates on the last decade, is used here as a tool for the analysis of the multiplicity of influences. Expanded several times a year, the TSB offers more than 7,000 annotated entries and uses a sophisticated key word system. The quantitative analysis of these key words and their thematic fields indicate the priorities in the dissemination of TS research over the past decade. How does research on translation today reflect this assumption? Is research on literary translation still as widespread as it was in the eighties? What is the relationship between publications on the ‘cultural turn’ and those on the ‘power turn’? The results of this analysis may indicate emphases and research priorities for the next decade in Translation Studies.
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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 | 0.062 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.046 | 0.068 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".