Bibliographic record
Abstract
Semantics study how language conveys meaning and is interpreted - examining word meaning, sentence meaning, pragmatics, and meaning representation. A shared understanding of word meanings is crucial for effective communication and social cohesion. Per Orwell’s 1984, political manipulation of language can shape public opinion and advance agendas through propagandistic euphemisms. Language manipulation undermines healthy discourse, critical thinking, and trust: recognizing these tactics is crucial for independent thinking. Understanding the impact of language is crucial for conflict resolution and promoting peace; clarity, empathy, and effective communication strategies enhance understanding. There is an ideological divide between academia and businesses, with a significant skew towards woke terminology and ‘far-left’ concepts. This "Woke Glossary" aims to bridge communication gaps and promote mutual understanding within academia and businesses via critiques. Fostering mutual understanding and effective communication should be our goals for promoting peace and cooperation, whereas woke terms often reinforce division using oppressor versus oppressed narratives.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".