Sociolinguistic Engineering of English Semantics as a tool for Population Indoctrination, Subjugation and Control
Bibliographic record
Abstract
The basic principles of modern sociolinguistic engineering as a tool for population indoctrination, subjugation, and control have their beginnings in the strategies designed by Joseph Goebbels of the NAZI regime and also those of the USSR. The redefinition of semantics is a dangerous tool used by propagandists to influence the individuals' sense of reality using language on a psychological level. This creates a populace that is more willing to follow harmful ideologies. The study will investigate existing legislation of Australia, the United Kingdom, the United States of America, and Canada about guarantees on free speech especially in academia, and the classification of hate speech. This study further looks at a microcosm of language used by the diversity, Inclusion, and Equity" movement focusing on an analysis of a glossary created by the University of Washington. It also discusses some terminology that is similarly erroneous but not included in the glossary. The history of terminology and their development is discussed as well as the scientific and linguistic validity of the provided semantic definitions in contrast to the original semantics. The study found that sociolinguistic engineering was taking place in universities and wider society which follows the historic pattern of the Third Reich and USSR. The study recommends that universities and education systems desist from such indoctrination and return to the traditional academic foundations of open inquiry and critical thinking.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".