The Bush vs. Gore Rhetoric After the 2000 Electoral Impasse: A <i>Ch'i-Shih</i> Analysis
Bibliographic record
Abstract
Ch'i , maneuverable energy flow or perceived vitality in a person or message, interacts with shih , advantageous strategic circumstance. Ch'i may create or enhance shih , and vice versa. In communication, one can boost the “message shih” by identifying with a value system or other favorable circumstances to enhance the ch'i of the communicator or the message. Following the 2000 presidential election stalemate, both George W. Bush and Albert A. Gore tried to persuade the public to support their position of recounting or not recounting the ballots. They both employed shih strategies that might enhance ch'i in their arguments. In a pioneering attempt, this article analyzes the two leaders' rhetoric through a ch'i-shih interaction model. The model includes four kinds of shih : sucking shih , including riding shih (manipulating external favorable shih to boost ch'i ) and driving shih (taking advantage of one's own favorable situation or shih to enhance ch'i ); bucking shih (going against the unfavorable strong position or shih to spark ch' i); ducking shih (averting unfavorable situation to maintain ch'i ); and constructing shih (creating favorable shih to boost ch'i ). This fourfold model of shih can establish ch'i communication theory as a more inclusive model of rhetorical analysis.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".