Bibliographic record
Abstract
English is spoken as a second language by many countries such as India, Pakistan, Bangladesh, Shri Lanka, Nigeria and Tanzania.In these countries English is spoken as a non-native or second language, and used for various purposes official, educational, social and interpersonal.In the countries such as Russia, Japan, Germany, France and Italy, English is used as a foreign language.A second language is one which is used for various purposes within the country while a foreign language is used.In learning a second language we will find that vocabulary is comparatively easy.However, in some countries like the United Kingdom, the United States of America, Canada and Australia, English is native or first language.As we listen to a person speaking our native language we hear not only what is said but also certain things about the speaker.English is first, second or foreign language.Children learn native language from a very early age to respond to sounds and tunes which their elders habitually use in talking to them.Second language is generally learnt later in life.The second language is learnt after the child has mastered the first language, his/her learning of the second language is influenced by the first 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.013 |
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".