Bibliographic record
Abstract
Purpose In the first two papers, the authors provided an overview of the research on followship and then presented a new model that extends understanding of it. This paper illustrates how the followship model can be used effectively to enhance organizations through coaching, mentoring, organizational change (enterprise‐wide reorganizations, mergers and acquisitions), high performer development, executive retention, new hire on‐boarding, leader development, and also in designing HR tools for performance management. Design/methodology/approach This is a capstone article. As such, it summarizes key points made previously, discusses existing HR practices and how they can be improved, incorporates case studies on followship, and illustrates practical applications. Findings Leaders must learn to model followship, and use it to solve staff performance issues. HR departments should include followship training to enrich development planning and, in the case of enterprise‐wide change such as mergers and acquisitions, speed and improve the results. Finally, providing followship training helps prevent executive derailment, improves Gen Y integration, and enhances the opportunities for high performers' career development. Originality/value This third and final article shows practical applications of ideas followship brings to organizational development. As such, it will be interesting to senior executives, high performance talent managers, executive coaches, and HR departments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".