DYSFUNCTIONAL LEADERSHIP: NOTES FROM THE “DARK” SIDE
Bibliographic record
Abstract
Some are born great, some achieve greatness, and some have greatness thrust upon them."-W.Shakespeare, Twelfth Night, Act II Scene v.Leadership has various permutations, including transformational, distributed and integrated authority, to mention just a few.Although these forms of influence may be viewed positively, not all leadership is positive.This article addresses the "dark" or dysfunctional side of leadership.Dysfunctional leadership is inherent in all forms of leadership, and exists independently of leadership style.Dysfunctional leadership comprises an amalgam of contextual conditions, personality traits and specific situational circumstances.Consequently, "dark" or dysfunctional leadership may be ameliorated or exacerbated by the type of task, personality of the leader, or even a mismatch of leadership style to specific contexts.Philosophies of leadership or issues of power may also derail positive leadership.Additionally, mis-educative strategies, such as binary thinking, template approaches and "decision driven data-making," as opposed to data-driven decision-making, reduce leadership to a dysfunctional enterprise.However, even dysfunctional leadership may not be entirely negative, as some experiences may be educative, although they may not be positive.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".