Bibliographic record
Abstract
The core of language, I shall argue, highlights what exists. Hence, prima facie my paper has nothing to do with language and relativity, and up to a point that’s true. An Inuit, an Indonesian and an Italian speak differently because they speak to different people of different things, and that makes them have different needs and wishes. If an Italian wants to speak to an Indonesian, he learns some Javanese. Because things have changed, many Indonesian and many Italians want to speak with more people, and with people living in far away places. That has turned into a lingua franca the language of the most powerful group, English, and the Indonesian and the Italian have a chance of understanding each other speaking English. Even if there are regularities in the environment and survival is fundamental for the Inuit, the Indonesian, the Italian and the American, there are huge variations through space and time – even in how to survive. Language is like food – we need water, vitamins, minerals, carbohydrates, proteins, fat, etc. How to get that depends from what is available where we live, how much of each ingredient depends on what we do and on our degree of fitness. If an Italian can assume his carbohydrates in Java eating pasta, it is because now they sell some Italian food even in some Indonesian supermarkets.\nThat is, I would not deny relativity, but would oppose the idea that relativity begins with 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".