Trust as a mediator of the relationship between character and perceptions of leader effectiveness during the COVID-19 crisis.
Bibliographic record
Abstract
Character is a leadership quality that is often scrutinized yet poorly understood Our research focuses on several questions relating to character and perceived leader effectiveness during the COVID-19 crisis First, does the character of the prime minister matter to voters during major crises such as the COVID-19 pandemic? Second, are all dimensions that comprise the leader character framework we examined considered essential for political leadership in times of crisis? Third, is character related to perceptions of leadership effectiveness? Fourth, what role does identification-based trust play in the relationship between character and perceptions of leadership effectiveness in times of crisis? The results of our survey taken during the early weeks of the COVID-19 pandemic reveal that character is considered among Canadians of voting age as an important ingredient of political leadership We also found that there is a significant gap between the perceived importance of the dimensions that comprise character and the belief that Prime Minister Justin Trudeau lives up to the expectations The congruence between the perceived importance of the character dimensions and the belief that Trudeau demonstrated these dimensions predicted leadership effectiveness, and this relationship was mediated by trust Our results are based on perceptions of leadership effectiveness;that is, we do not have objective measures of performance (PsycInfo Database Record (c) 2021 APA, all rights reserved) Impact Statement This study advances our understanding of the importance of character in political leadership during crisis situations such as the COVID-19 pandemic Our findings explicate the behaviors associated with leader character Additionally, the study reveals that trust helps to explain the effect of character on perceived effectiveness of leaders (PsycInfo Database Record (c) 2021 APA, all rights reserved)
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".