Sticking one’s nose in the data : Evaluation in phraseological sequences with nose
Bibliographic record
Abstract
With the realization that introspection and the use of dictionaries constitute a \nprecarious foundation for studies of metaphor and metonymy, corpora have in \nrecent years been used increasingly in the endeavour to explore the authentic use \nof figurative language (see, e.g. Deignan 2005; Stefanowitsch and Gries 2006). \nSimilarly, investigations of phraseology (e.g. Moon 1998) have come to rely \nheavily on modern large-scale corpora, while analyses of evaluative lexis in the \ntradition of John Sinclair (e.g. Sinclair passim; Hunston and Thompson 1999; \nStubbs 2001) have a theoretical commitment to the corpus as an indispensable \ntool. The present paper brings together these theoretical strands. \nIt is commonplace in cognitive linguistics that human cognition is embodied \n(cf. Lakoff and Johnson 1980; Langacker 1987, 1991; Kövecses and Szabó \n1996; Gibbs and Wilson 2002; Gibbs et al. 2004). Therefore it is no surprise that \nmany phraseological sequences are built up around words related to the body, \nand in this corpus-based case study we have chosen to focus on the evaluative \nfunctions of metonymic and metaphorical sequences containing the noun nose. \nIn comparison with other body parts, such as the hand and the mouth, the nose is \nfairly restricted in its use. Whereas in some cultures, like Maori and Inuit, the \nnose has an additional social importance as it is used for greeting, in western \nsocieties the nose seems to have predominantly negative or humorous connotations \n(cf. Gogol’s The Nose). One can only speculate about the reasons for this: \nperhaps it is the predominance of bad smells, or the association with snoring and \nthe excretion of mucus. Some sequences containing nose also imply that the \nagent is behaving like an animal. As an example of the latter type of connotations, \nconsider (1) to (3) with the metonymic sequence stick one’s nose somewhere. \nThis sequence is most frequently negative, as in (1), sometimes slightly \nironic, as in (2), and occasionally positive, as in (3) (see further section 4.1.1). (1) The Steinbrenner we remember was always sticking his nose in where \nit’s not wanted. (NYT 1996) \n(2) As soon as it’s nice enough to stick your nose outside, this place is \npacked (…) (Ind 2000) \n(3) Stick your nose in it. Grind it out. You can’t be turning the other cheek \nall the time. (NYT 1990) \nIn contrast to most studies of evaluative language we will consider both \ninstances where speakers express their opinions about other people’s activities \nand cases where the disapproval is on the part of the agent in the clause without \nthe speakers conveying their opinions of this.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".