Unmasking Obsessive Compulsive Behaviors in Leaders – A Dark Side of Leadership
Bibliographic record
Abstract
Individuals in the workplace interact with peer constituents displaying various personality behaviors that influence productive workplace relationships. Sometimes, the darker side of leadership is masked and camouflages disturbing personality behaviors. Leaders are no exception to the array of personalities hindering productive follower relationships. Obsessive-compulsive disorder (OCD) is an anxiety disorder characterized by recurrent, obsessive thoughts and compulsive behaviors. Obsessive-compulsive disorders (OCD) in leaders manifest themselves in a variety of ways and have the potential to deter satisfactory relationships with followers. Subordinates are subjected to higher instances of employee surveillance, matriculate attention to detail, extraordinary communication and feedback, repetitive reprimands, and fear tactics to motivate desired behavior. Although these practices are within management scope, manifestations of OCD leader tendencies and their effect on followership cannot be ignored. As the level of anxiety in Americans continues to increase, organizations may observe higher instances of OCD-related behaviors in leaders. This paper addresses OCD behaviors in leaders and its effect on followership and productivity.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".