Metaphorical Uses of Proper Names and the Continuity Hypothesis
Bibliographic record
Abstract
Abstract According to proponents of the continuity hypothesis, metaphors represent one end of a spectrum of linguistic phenomena, which includes various forms of loosening/broadening, such as category extensions and approximations, as well as hyperbolic interpretations. The continuity hypothesis is used to establish that the inferences derived from the set of linguistic expressions mentioned above result from the same or nearly similar pragmatic processes. In this paper, we want to challenge that particular aspect of the continuity hypothesis. We do so based on considerations and analysis of an understudied linguistic phenomenon that we call the metaphorical uses of proper names (MPNs). We first explain how MPNs represent a unique linguistic class distinguishable from, for example, nicknames. In addition, we offer some remarks on how MPNs can be understood against the background of current debates between referentialists and predicativists about names. Our discussion leads us to conclude that MPNs are categorically different from literal interpretations of proper names. We spell out the consequences that the results of our analysis have for the continuity hypothesis.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.004 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".