Bibliographic record
Abstract
In his recent book entitled Metaphor, Hungarian Lakoff-scholar Zoltán Kövecses translates the tenor-vehicle relationship into a linguistic Great Chain of Being (2002).The primary purpose of the paper is to examine how Canadian metaphors of weather fit into this framework.The first part of the paper presents some theoretical grounding, proceeding from the overt-covert and direct-indirect relationship of tenor and vehicle to Lakoff's cognitive concept of metaphor (1980, 1993).Based on this concept, the linguistic Great Chain of Weather Metaphors is created.The second part of the paper makes an attempt at examining the most typical source and target domains of weather, and, based on a pilot sample, it also looks into conceptual weather metaphors built by mapping at each level of the Great Chain of Weather Metaphors.Furthermore, the analysis tackles the question of conventionality as well as the establishment of a certain hierarchy among the different Great Chain levels through the employment of Ricoeur's Platonic ladder theory (1987) and Lakoff's principle of unidirectionality (1990).This section of the paper is followed by an in-depth analysis focusing on objectto-weather and weather-to-object correspondences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".