The Easiest Common Language in the Cosmos and a Manifesto of the Human Common Language
Bibliographic record
Abstract
The author of this essay has first probed into the human beings’ necessity for the common language, and has expounded on the easiest common language existing everywhere in the cosmos and running through all “dead” & living things, and then has listed its very-widely-used and never-exhausted applications. Based on his many years’ researches and experiments, the author has pointed out that the universal language exists in the human brain and the human languages, expecting to solve the long-suspending and unsettled technical problems in the language researching and teaching. The essay has sketched a prospect of the human common language, and one practical and scientific route – adopting the universal language and the human brains’ laws and the excellences of the-most-widely-used language can bring about the formation of the human common language, and finally called on every nation and every earthman to do a bit for the formation. Key words: Cosmic common language; Brain and sound language; Laws; Human common language
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".