Bibliographic record
Abstract
Our limited vocabulary should not constitute a prescriptive curriculum, but instead point to our insufficiencies and our need to expand the field of epidemiology In summation, I have only one question: is Latin dead? Max Fischer1f It is said that the Canadian Inuit have more than 50 words for “snow”, but none for “pollution”. In fact, the romantic idea that the Inuit can differentiate between 50 types of snow is not strictly true; the multitude of snow words is related to the way in which suffixes are sequentially added to root words in the Inuktitut language to create new expressions. Nevertheless, this idea serves to show that, although in certain cultures we have many words to describe things that are relevant to our everyday experiences, we tend to lack words for concepts with which we are unfamiliar. To give another example, there is apparently no formal term for “dyslexia” in the Chinese language, a condition that is described with a four-character phrase ![Graphic][1] roughly translating as “reading impairment”. This is partly due to the relatively recent recognition of reading dyslexia in Asian countries, itself related to the lower prevalence of this condition in these settings.2 This example is interesting because it highlights cultural differences in dyslexia and because the terms themselves tell us much about how meaning is constructed in different languages. Recent evidence shows that different parts of the brain are affected in reading dyslexia among Chinese as compared with English school children.3 The higher prevalence of reading difficulties among speakers of alphabetic languages such as English is thought to be in part due to the fact that letters must first be processed into sounds, and combinations of letters into combinations of sounds that carry meaning. English poses particular difficulties at an early age, because of … [1]: /embed/inline-graphic-1.gif
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.627 | 0.491 |
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".