Bibliographic record
Abstract
Purpose The purpose of this paper is twofold: first, to provide a compelling argument that followership has significant practical value in enhancing career and organizational value; and, second, to encourage dialogue about followership. Part 2 will extend current ideas about followership to provide a more comprehensive, holistic model. Part 3 will show how the model can be used as a training tool, in mentoring, for performance appraisals, and in designing HR solutions. Design/methodology/approach The strengths and weaknesses of current theories are highlighted, motivating both the need for making followership more visible within an organization and the need for a more comprehensive model. Findings Good followers report higher career satisfaction, get promoted more often, and add greater value to their organizations. Moreover, followship skills can be developed. Originality/value Previous research has focused on followship as either a fixed set of behaviours or traits, or as something a leader has to learn to manage. This is the first paper to empower followers – everyone in an organization is a follower and followership skills can be learned. As such, the three articles are of particular interest to senior executives 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".