Bibliographic record
Abstract
Two things happened to me recently that made me reconsider the turbulent vastness of language that can be spoken, scratched in dirt, bloodied onto walls, written, stuttered, laughed, gestured, sung.-Hoogland (2003, p. 11) Literature's specialized use of language is its art.Poetry is the most heightened use of literary verbal structures that thrive on immediacy, multiplicity of meaning, colour, sound and other sensory data, emotion, and images of the concrete, material world.Its conventions include similes, assonance, dissonance, diction, fantasy, make-believe, puns and word play, and most notably, metaphor.In its broadest definition, metaphor is speaking of one thing as it is experienced in terms of another.Zwicky (2003) says metaphor "is one way of showing how patterns of meaning in the world intersect and echo one another," (p. 6) and also, is a "species of understanding, a form of seeing-as: it has, we might say, flex.We see, simultaneously, similarities and dissimilarities" (p.4).Of course, metaphor is neither limited to aesthetic language, nor to language; but of all the language forms it is literary language that makes metaphor its centre of gravity.The general field within which the literary arts occur is called aesthetics, that is, a field of practice that values imaginative, sensory, and emo-
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.041 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".