Bibliographic record
Abstract
The research I was involved in mainly focused on the aspects of destructive or toxic types of leadership and their effects on the organization’s performance. The goal of this research was to call attention to such shortfalls in leadership and devise solutions to mitigate organizational downfall. In addition, our research has investigated the construct of hope and how hope can be mobilized to foster positive well-being and by extension, effective leadership. By making hope a foundational guiding tool in the practice of leaders, they will gain agency in their work and create a reality that reflects their goal of positively shaping the future of their followers, instead of becoming derailed by circumstances beyond their control. Our research will call attention to broader measures of success in organizations, namely hope, to illustrate how hope-growing leadership can provide new insights for sustainable efforts to organization effectiveness. Our hope is that this research will benefit stakeholders in various domains, including education, medicine and mental health. It will also explore the concept of hopelessness and its detrimental consequences on mental health and performance. By examining current theoretical frameworks and empirical studies, this review aims to synthesize how hope functions as a protective factor and a developmental asset in personal and professional contexts, while also highlighting the detriments related to its absence.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".