Conceptual Metaphor in Chinese and American Entrepreneurial Image Construction From the Perspective of Framing Theory: The Case of Ren Zhengfei and Tim Cook
Bibliographic record
Abstract
Entrepreneurial image nowadays is closely related to the company’s business performance as speaking in public is a common phenomenon for entrepreneurs to make or break their companies. The present study aims to conduct a comparative study on the impact of conceptual metaphor on entrepreneurial image construction, through careful analysis of 15 Chinese transcripts of 23286 words and 15 English transcripts of 22175 words from Ren Zhengfei and Tim Cook’s interview, keynote speech, commencement address, and speech on product release conference from October 2018 to December 2020. Employing framing theory and conceptual metaphor frameworks, the author examined the metaphorically used expressions with the help of MIPVU, explored how conceptual metaphors play a crucial role in images construction and unpacked national or corporate values for different conceptualization of the same source domains.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".