Operationalizing grammatical metaphor
Bibliographic record
Abstract
Abstract Nominal grammatical metaphor (NGM), as theorised within systemic functional linguistics (SFL), typically refers to cases where a process is ‘metaphorically’ encoded through nominal resources, rather than its typical encoding through the clause. Previous approaches to the study of NGM have generally relied on the concepts of agnation and congruence, but these concepts can be quite subjective in nature, leading to an unstable account of the phenomenon. This paper evaluates the theoretical status and methodological implementation of NGMs. Our aim is to consider whether Aktionsart can offer a means of operationalizing NGM. We analyzed 492 nouns from a random selection of 200 sentences in the PopSci register of the CroCo corpus. The nouns were analyzed separately by different coders in terms of (i) grammatical metaphor status and (ii) their ontological status combined with an analysis of Aktionsart. Our statistical analysis shows that adopting an Aktionsart approach provides a more nuanced and anchored methodology for the analysis of NGMs as it not only provides a more consistent and robust analysis, but it also enables us to specify subtypes of nominal grammatical metaphor based on their subtler semantic characteristics.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".