The Good Guys and the Bad Guys: The Behavior of Lenient and Demanding Translation Evaluators
Bibliographic record
Abstract
The behavior of demanding and lenient evaluators is analyzed and discussed. Little is known about the process of translation evaluation, specifically on how different types of evaluators perform. The 88 subjects of this study were classified as demanding or lenient on the basis of the average quality judgments they made on 48 translated texts. Their profiles were outlined according to a series of parameters and categories starting from the observation of their products, i.e., the evaluated texts. Lenient evaluators carried out more actions on the text, were fairly product-oriented, showed a fairly steady performance, seemed to be more confident, and were probably more committed to the evaluation assignment they were given in this research. Demanding evaluators intervened less, were usually feedback-oriented, preferred to carry out actions in certain segments and text parts, expressed less certainty, and were possibly more aware of the particular circumstances surrounding the experiment. While demanding evaluators appear better suited for professional environments and advanced level teaching, lenient evaluators seem more suited to research and teaching at initial stages. The present work might pave the way for further research into evaluative profiles.
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 | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".