“I Do not Let Setbacks Discourage Me Much” The Composition of a Finnish Female Leader
Bibliographic record
Abstract
The article illustrates findings from a larger study on the Finnish female leaders’ paths to a leader’s position. This sub-study analyzed the composition of a Finnish female leader as narrated by leaders themselves. It was studied through two specifying questions: what kinds of personal features female leaders considered important in their leadership practices and how do these women describe the emphases of leadership. This was a qualitative study in which 10 Finnish female leaders were interviewed in two phases in 2006 and 2011. According to the narratives, the important personal features were perseverance and rabidity, honesty and humbleness, as well as tolerance of criticism, adversities, and loneliness, which were analyzed in the light of the authentic leadership theory. The emphases of leadership included the dimensions of social interaction and being a bellwether. The study contributed a new narrative viewpoint to the definition of authentic leadership.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".