Bibliographic record
Abstract
The article reveals linguistic, cognitive and semiotic characteristics of simile in English poetic texts of Canadian poetry. Cognitive peculiarities of simile are defined via cognitive and semiotic operation of comparison and reconstruction of concepts and conceptual metaphors which underlie it. The cognitive operation of comparison is aimed at obtaining new knowledge as a result of matching homogeneous or heterogeneous comparable entities. Linguocognitive operations of analogical and narrative mapping are derived from the basic cognitive operation of comparison. Linguocognitive operations of attributive, relational, systems mapping are aimed at clarifying the basis of comparison, which establishes the similarity between subject and object of the simile. Semiotic peculiarities are clarified via identification of cultural codes and word-symbols which are represented in subjective and objective parts of similes. Thus, I view simile as a multifunctional linguistic and semiotic construal representing verbal patterns of form and semantic function. Linguistic and semiotic analysis of similes enables their classification into iconic, indexical and symbolic ones. The semiotic classification of similes is premised on the type of semiotic relation that exists between a “sign vehicle” and its meaning. Iconic similes are based on similarity between a sign and its referent, while indexical similes show relations of contiguity. Symbolic similes are differentiated on the basis of conventional pairing of a signified and a signifier within a simile
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".