Creating New Abilities with Language
Bibliographic record
Abstract
This article explains how new abilities are acquired when speaking a language that I call the Soral language.The language is created with the aid of computer technology that organizes the words in a way that enables our brain to attach meaning to the sound of the words.The following abilities are acquired when speaking the language.1.The ability to understand the meaning of words that have never been heard before or seen in a book.2. Improved ability to recall words from memory.3. Larger vocabulary.4. Improved conversational skills.5. Improved understanding of the meaning of words.6. Ability to understand medical and other technical terms without formal training.7. Ability to learn the language more easily than other languages.Words in the Soral language are created by entering words in one of our existing languages into the computer program, then clicking buttons which are hyperlinked to other pages containing more buttons.Each button describes one characteristic of the entity that the word represents.A letter is assigned to each characteristic.By clicking on several buttons in succession a word is created that has a sound that represents the characteristics of the entity that the word represents.Speakers of the language can understand the meaning of words by the sound of the word.It is proposed that the Soral language should be adopted as a universal 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".