Research on the factors influencing the frequency of intended wrongdoings behavior of the managers
Bibliographic record
Abstract
After doing some research on the behaviors of the managers, the author found out that there exists a kind of intended wrongdoings behavior among the managers, belonging to the “Irrational behavior”, which is different from the “Rational person” and “Limited rational person”. It can be indicated from the research that the intended wrongdoings behavior of the managers are mainly impacted by four factors, which are go-as-you-please attitude, personal preferences, managing rights and the ability of the managers. There are some related strict conditions such as rational decision making ability, desire-controlling ability, right restricting ability as well as morality restricting ability. Key words: irrational behavior, intended wrongdoings, frequencies, influencing factors Resume: A la suite des recherches sur des comportements des directeurs, l’auteur a trouve qu’il y a parmi eux une sorte de comportement malfaiteur voulu appartenant au “comportement irrationnel”, qui est different de “l’homme rationnel” et de “l’homme rationnel limite”. Il resulte des recherches que les comportements malfaiteurs voulus des directeurs sont principalement influences par quatre facteurs: attitude d’agir a son gre, preferences personnelles, pouvoirs de direction et leur capacite. Il existe des conditions strictement concernees telles que les capacites de la prise de decision rationnelle, de controle, de la restriction du pouvoir ainsi que de la restriction de la morale. Mots-Cles: comportement irrationnel, malfaits voulus, frequence, facteurs influants
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".