The experience of leadership through difficult situations : what helps and hinders
Bibliographic record
Abstract
This research examined leadership in difficult situations using phenomenological and critical incident analyses of 14 interviews with respected leaders. Leadership in difficult situations was examined as a personal experience consistent with the perspective of counselling psychology. The phenomenological analysis has produced four voices. The first voice captures the participants' description of leadership as a paradox of personal and situational dynamics. The second voice forms an uncommon elucidation of the lived experience of leadership in difficult situations. The participants are seen to be striving in a resilient manner characterized by a sense of acceptance and authenticity. The third and fourth voices describe the means by which the participants are effective in difficult situations. The third voice captures the intra-personal process of resonance, personal and social awareness, personal supports, mastery of the issues, and the capacity to generate remarkable outcomes. The fourth phenomenological voice captures the participant's active engagement in connecting with people, shaping the work context and leading collaboration to develop a successful process. Five categories have been produced by the Critical Incident Technique, which examined what helps and what hinders in the experience of leadership through difficult situations. The five categories are: being experienced, principled and self-aware; having personal supports and influences; being interested and skilled in connecting with people; being both energetic and able to withdraw appropriately; finally, actively cultivating their perspective or vision. These five categories indicate the capacity of the participants for diverse means of participation in difficult situations. Results are discussed in terms of implications for research and practice.
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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".