Bibliographic record
Abstract
LEADERSHIP AND FOLLOWERSHIP While the focus of understanding leadership has typically been understanding who a leader is, what characteristics are possessed by the leader, what a leader does, and the match between leadership behaviour and the situation, a more recent trend in the leadership literature is to understand that leadership does not function in isolation - rather, it is part of a dyadic relationship with followership. The leader comes to rely on those who provide this support, which in turn can provide a positive exchange for both leader and follower. [...]followership can actually direct leadership through its expectations and can also, in part, explain leadership behaviours and effectiveness (Wang, Van Iddekinge, Zhang, & Bishoff, 2018). Sometimes leadership is given based on an expectation that there will be a reward provided for following a particular individual; or alternatively, in some cases, leadership is given through fear that there will be punishment or retribution if followership is not provided. [...]expectations inherent in particular situations can impact who is chosen to be a leader and the extent to which others are willing to follow that individual. LEARNING ABOUT LEADERSHIP AND FOLLOWERSHIP While every individual holds implicit ideas of what leadership is and is not, and what makes leadership a success or a failure, examining leadership through the lens of research helps to establish more generalized views of leadership, and can also aid in understanding how followership develops, and the role of followership in developing leadership.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".