Being Optimistic About Inclusion: Biden’s Rhetorical Strategy of Positive Self-Presentation Reflected in Teacher Training Policy
Bibliographic record
Abstract
This paper reveals how Joseph Biden creates an image of a president-optimist through rhetorical strategy of positive self-presentation by using language effectively in his inaugural speech. The strategy is realized by two tactics – positive representation of compatriots and forecasting good future for the USA. Aimed at evoking in the audience optimistic thoughts and moods, these tactics ensure appropriate use of rhetorical devices. In the tactics of positive representation of compatriots presupposing describing American citizens in a good light, Biden employs predominantly hyperboles (4 occurrences), metaphors (5 occurrences) and repetitions (12 occurrences). The tactics of forecasting good future for the USA consisting in describing positive experiences, wellbeing and prosperity in the future is realized by repetitions (29 occurrences), antitheses (27 occurrences) and metaphors (11 occurrences). Drawing on the rhetorical devices in the analyzed texts it is revealed the tactics of discrediting the opponents, the tactics of demonstrating power and others which obviously contribute to other strategies. This research demonstrates that Biden is likely to present himself positively in an attempt of maintaining power and support. The paper also discusses electoral promises and steps made by Biden’s administration to improve the quality of education based on the principles of equity, diversity, and inclusion. It is established that growing investment in education is vital and teacher development should receive special attention on organizing a safe inclusive learning environment.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".