Bibliographic record
Abstract
The paper aims to determine top languages in global information production and the ratio of information resources available in those languages. The scope of the study was limited to information resources, which are commonly available through the public domain, i. e. libraries and the Internet. They include books, academic journals, newspapers and popular magazines, films, and web pages. The summarized results were compared with the percentage of literate population in each corresponding language. The paper suggests that there is a significant gap between the users of information and available information resources. 82% of all information in the world is produced in top ten languages. Countries with low literacy rate and poor education are excluded from universal knowledge. English constitutes almost half of world’s information resources. The educated community tends to consider English as a universal language. At the same time, non-English resources are largely ignored in English-speaking countries. The term “language divide” can be equally applied to the English-speaking world. The paper outlines further research directions. The early version of this paper was presented as a poster session at the CLA Conference in Vancouver in May 2008.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".