Leading through belief: managing the power of hope
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Leaders have long understood the importance a belief system has on the productivity of their team. The authors explain how can such an intangible motivational force be addressed and how leaders have the capability to influence a firm's success by inspiring positive beliefs. Design/methodology/approach Belief management involves recognizing those beliefs that both hinder and promote the advancement of a leader's vision. This includes the leader's beliefs as well as those of the team. Findings To begin managing beliefs, executives should take three initial steps: identify core belief, ask others what they believe, brand your beliefs. Research limitations/implications Dr Gregory Berns, a psychiatrist and neuroscientist at Emory University in Atlanta mapped the neurological effects of a belief exercise on his test subjects. Through the use of magnetic resonance imaging, Berns could see specific changes in cellular activity. Practical implications There's new evidence that a leader's beliefs are the foundations for each team's aspirations. Originality/value Leaders must not only tell people what they believe but let them know why they believe. If managed correctly, these beneficial beliefs will spread throughout a company to all its stakeholders.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it