Building up Open-Ended Empirical Modules in Translating: A Case Study of “Logical Meaning Extensions”
Bibliographic record
Abstract
This paper brings in the idea of building up “Open-ended Empirical Modules” (OEEMs) as a translation skill supported by the theory of contextual parameters. With examples being subcategorized into over twenty empirical rules, the study constructs an open-ended module of Logical Meaning Extensions (LME) as a representative paradigm and presents the know-how and know-why expertise. It is methodologically notable that the case analysis and demonstration of meaning extensions from concepts in SL to those in TL are conducted in a procedural way in which the cognitive mechanism of inferential processes of LME is verifiably explored. The significance of this research is seen in its display of a systematic way of generalization and classification of empirical rules for translation skills in teaching and learning translation. It may also provide translators with a possible method to follow in generalizing empirical rules from their own practice to enrich translating skills.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".