“Know Your Coffee!” The Cultural Semantics of a Lexico-Syntactic Molecule of English
Bibliographic record
Abstract
This paper presents a cultural semantic analysis of the English syntactic construction ‘know your + noun’ made combining the analytical principles and methods of ethnosyntax (Wierzbicka, 1988, 2003, 2006a) with those of corpus-based discourse analysis (Baker, 2006; Partington et al., 2004). Three main points are made in the paper: (i) ‘know your n.’ constitutes an indissoluble lexico-syntactic molecule of English expressing its own specific meaning; (ii) this construction is both genre-specific and subject to intralinguistic variation; (iii) this construction is quintessentially Anglo, because it reflects Anglo cultural assumptions about personal autonomy informing certain speech practices in English discourse (Goddard & Wierzbicka, 2004; Wierzbicka, 2006b) and defies easy translation in other languages. The analysis is based on the findings of a corpus search in GLOWBE across varieties of English complemented by additional data from the web. The results provide a clear picture of the meaning of ‘know your n.’ and of where it situates within the broad range of know-constructions. Ultimately, the paper emphasises the contribution that corpus-based, empirical discourse analysis can make to the semantics and ethnography of syntax as well as to the study of the interface between syntax, semantics and culture.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".