Second Acts and Second Chances: The Bumpy Road to Redemption
Bibliographic record
Abstract
Throughout history, there are numerous examples of business and government leaders who have fallen from grace only to rise again, and have a “second act” and a “second chance” as a legitimate social actor or leader—that is, they achieved redemption. We explore “the road to redemption” of leaders—when and why it occurs, and what “bumps” prevent it. In our analysis, we conceptualize redemption as a process with three elements—remorse, rehabilitation, and restoration—and as an outcome (the restoration of legitimacy). We argue that achieving redemption is not a product of chance; rather, it is a social construction process of narrative creation and identity construction involving many parties. Also, the road to redemption is shaped by cultural-specific factors—and it is temporally dependent. From this framework, we identify new directions for the theory and practice of leadership.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".