Manipulation: The Key Success of the World’s Quasi Lords
Bibliographic record
Abstract
It is very challenging to recognize manipulation as such; however, once humanoids become aware of it, they realize that they were subjected to manipulation. Sadly, the degree of manipulation is skyrocketing to the point that someone could argue that almost all activities are more or less manipulated. Individuals, families, nations, states – almost all are impacted by manipulation of world’s quasi lords who are driven by their own personal interests and are willing and ready to break all rules of humanity and decency for the benefits of their own personal interests and agendas. The primary aim of this paper is to demonstrate that there is a need to fight manipulation, particulary manipulation of Bosnia and Herzegovina and the whole world. In order to do that, this paper elaborates on the most important charateristics of manipulation. An emphasis is paid to the types, methods and strategies of manipulation so to understand the implications of manipulation. Lastly, this paper proposes measures such as immunization in order to reduce humanoid proclivity to be manipulated.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".