Bibliographic record
Abstract
<p>Inflation is often regarded as a dangerous phenomenon which poses a potential threat to economies in the world. It is thus an entity that demands the constant attention of economists, policymakers and the general public. In order to make this abstract entry more concrete and vivid, a number of metaphorical expressions are used to depict inflation. Building on previous studies that relied on researchers’ intuition or a small corpus, this paper sets out to investigate the use of inflation metaphor by examining a 450-million-word Corpus of Contemporary American English (COCA). The results show that there exist a considerable number of metaphorical collocates of inflation, which can be grouped into separate yet closely related categories: INFLATION IS FIRE, INFLATION IS DISEASE, INFLATION IS AN ANIMAL, INFLATION IS AN ENEMY, INFLATION IS A RACER, INFLATION IS A MACHINE, and the like. This study shows how these metaphors function in specific contexts, and how they structure and reframe our thinking about inflation and other related economic concepts. These findings have pedagogical implications for both teaching of economics and second language learning of relevant words and phrases.</p>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".