Constructions of Solidarity and Leadership of Powerful Global Leaders in Post Pandemic Recovery Speeches
Bibliographic record
Abstract
The COVID-19 witnessed varied enactment of leadership by political leaders around the world in response to its threat. With COVID-19 recovery policies shining a spotlight on government’s future action, the leadership of global political figures is once again scrutinised on how they ‘build back better’ the damages caused by the pandemic. This study analyses the COVID-19 post- recovery speeches of the world’s most powerful leaders to gain an understanding of their enactment of discursive leadership. Focusing on solidarity, this study elucidates the processes and identifies how it is linguistically constituted as part of their aims to create bonds with international allies. The analysis reveals that the construction of solidarity is done through storytelling and, proverbs and metaphors. The findings have led to a deeper understanding of discursive leadership and solidarity practices in political discourse, and is hoped to be useful to researchers to understand exemplary discursive practices pertinent to solidarity building.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".