The Definition of Translation in Davidson’s Philosophy: Semantic Equivalence versus Functional Equivalence
Bibliographic record
Abstract
This article discusses how, in addition to providing a definition for translation, the concept of equivalence may explain why we can say that sentence S in language L is a translation of sentence S 1 in language L 1 . It analyzes two main kinds of equivalence that are used in analytical philosophy to define translation: semantic equivalence and functional equivalence. This analysis shows that drawing a distinction between semantic and functional equivalence is a way to understand the distinction between different levels or aspects of meaning. Both semantic equivalence, introduced by Gottlob Frege, and functional equivalence, proposed by Wilfrid Sellars, were developed in Donald Davidson’s theory of meaning. After discussing the limits of Davidson’s definitions of equivalence, this article will argue that functional equivalence is a reason for comparing Davidson’s philosophy to positions such as those expressed by Hans-Georg Gadamer’s hermeneutics.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".