Bibliographic record
Abstract
The article investigates linguistic sources about the language which have been investigated by the linguists for many years. The author has been studying linguistic sources comprehensively since the time of V. fon Humboldt, F. de Saussure etc. The main purpose of the author is to search the essence of the language and try to find the answer of the question how the language was evoluated. The questions “how life began? how the universe began? and how the language evoluated?” are highlighted in the given article. The author gives explanation to two theories about the essence of the language. The first one is the theory about the essence of the language which is supported by N. Chomsky and his followers. The content of their theory is that the language is innate. N. Chomsky always emphasized that the language is at least as much a system structuring and thinking about the world as it is a vehicle for communication. Though some linguists don’t agree with this idea. The second theory which is supported by a biologist Derek Bickerton and others is that the language is not innate. The author gives her comments on both of the theories. Sometimes one theory wins, sometimes the other one. But no concrete result has been found yet, either by linguists or by bilologist, etc.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.035 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".