The Authenticity Challenge: How a Value Affirmation Exercise Can Engender Authentic Leadership
Bibliographic record
Abstract
In this paper, we introduce a brief and effective way to increase leader’s perceived authenticity in the form of a values affirmation exercise. Personal values reflect what people consider as most important and ideal (Rokeach, 1973). As a result, values affirmation exercise where people are reminded of their values activates their ideal selves. In Experiment 1, we show that values affirmation induces the feeling of authenticity by activating the ideal-self. In Experiment 2, we show that values affirmation not only induces the feeling of authenticity but also help individuals convey felt authenticity to others and be perceived as authentic. In a leader- follower communication, we find that leaders were perceived as more authentic by audience members when they thought about their personal values prior to engaging in communication with them. Additionally, we examine the role of leader’s communication skills in gaining perceived authenticity and find that leaders with good communication skills are viewed as more authentic. We also find that after values affirmation, leaders are viewed as authentic even when they lack good communication skills.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".