Equivalence and polyvalence: A case for the stratification of semantics
Bibliographic record
Abstract
The article presents a semantic theory suggesting that equivalence and polyvalence are closely connected phenomena. The connection between them is utilised by making equivalence the basis of an argument for polyvalence within semantics, or in other terms, for a stratified semantics encompassing two levels of description. This argument from equivalence lends new support to frameworks assuming a stratified semantics. For the argument to work properly, it is necessary to distinguish between equivalences that are facts of language and equivalences that depend on extralinguistic circumstances. A few other requirements for a coherent concept of equivalence are also introduced. A major part of the article is devoted to an investigation of previous accounts of equivalence and of the conceptual underpinnings of these accounts. The investigation shows that all previous accounts have failed to make the necessary distinction. Close examination of various cases of equivalence reveals differences that are captured by the proposed distinction, thus providing evidence for its accuracy. The article concludes that the argument from equivalence strengthens the case for a stratified semantics provided that the concept of equivalence has been appropriately delimited and defined.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".