Metaphor and Framing in Cognition and Practice: Take Metaphors for AIDS as Examples
Bibliographic record
Abstract
The notion of “framing” as an important function of metaphor can be applied to the related perspective: cognitive and practice-based. We analyze these perspectives by applying it to a corpus-based study according to Corpus of Contemporary American English (COCA) and English Web 2015 (enTenTen15) Corpus in Sketch Engine of illness-related for AIDS concordances and collocations and demonstrate its value to both theory and practice. By analyzing the data which includes violence-related metaphors for AIDS and through the application of this framework, we can find that there are merits in applying the notion of framing at different levels of generality in metaphor analysis (conceptual metaphors and linguistic metaphors), so that we can have a deeper understanding of cognition and framing in AIDS. Metaphor has characteristics of salience and mutual reactions, therefore, this article tries to study metaphor from the perspective of frame theory so that it can provide a new angle for researching metaphor. According to theoretical and practical advantages of taking two levels into account when considering the use of metaphor for communicating about sensitive topics such as AIDS and people’s positive, negative or neutral attitudes towards AIDS. We emphasize that there is a need for “rich” definition of framing when evaluating, comprehending and commenting.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".