From Prison to Putrajaya: Examining Narratives and Enactment of Authentic Leadership in Anwar Ibrahim’s Post-Election Speech
Bibliographic record
Abstract
Studies on leadership and stories have become increasingly popular as evidenced by a growing number of research in that area. It is notable that, while these political actors construct credibility and affiliation with the public, the deployment of narratives can reflect their enactment of leadership and leadership styles. The present study is an example of how narratives within one’s political rhetoric set up a normative example of how leadership is enacted. This research utilises a speech delivered by Anwar Ibrahim, the current Prime Minister of Malaysia to examine the embedded narratives and how they reflect his authentic leadership style. The study analysed one of the first speeches that he delivered after winning the 12th General Election in 2023 with a duration of 81 minutes. Taking narrative as a social practice and utilising the narrative framework by Labov and Waletzky (1967), the analysis reveals the values embedded within the narratives are aligned with authentic leadership. They include openness, morally responsible, representative for the rakyat (people), and a role model exhibiting resilience and positive attitude, hence rebuilding Anwar’s tarnished image in the eyes of the conservative Malay Muslims. By demonstrating how narratives can restore legitimacy and rebuild trust, this study contributes to the growing body of research on authentic leadership. It highlights the importance of discourse analysis in capturing leadership-in-action and provides insight into the role of political storytelling in Malaysian democracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".