Exploring Implicit Belief Alignment in Leaders and Followers
Bibliographic record
Abstract
Through quantitative, nonexperimental research, this study focused on follower schemas among leaders and followers. The sample included 203 leaders and followers from Canada and the United States. The research addressed a literature gap through comparing leaders and followers’ implicit beliefs. In followership literature, two prominent areas of study regarding followers’ implicit beliefs are the implicit followership theory (IFT) and follower role orientation. Although many scholars have considered IFT and role orientation as the same construct, no scholar has ever compared the theories for correlation. Thus, the study addressed another literature gap through correlation and predictive analysis tests to compare between the two constructs, which were the instruments that measure IFT and role orientation: the implicit followership scale for IFT and the coproduction and passive role orientation scale for follower role orientation. The results showed no statistical difference between leaders and followers regarding IFTs and follower role orientations. The prototypes from the implicit followership scale showed no correlation to coproduction role orientation. There was, however, a correlation and a predictive relationship between the antiprototypes from the implicit followership scale and passive role orientation. The findings are valuable for individuals, teams, leaders, followers, and organizations.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".