German is not Necessarily more Difficult than English: Evidence from a comparison among English, German and Hanyu Pinyin L'ALLEMAND N'EST PAS FORCÉMENT PLUS DIFFICILE QUE L'ANGLAIS: CONCLUSION TIRÉE D'UNE COMPARAISON ENTRE L'ANGLAIS, L'ALLEMAND ET LE PINYIN
Bibliographic record
Abstract
In the area of foreign language learning in China, it is a widely-received view that German is more difficult to learn than English. Few people have realized that the factors that make German difficult to learn can in fact make it easier to learn. This article argues that it is not necessarily the case. Through comparing three pairs of relations in German, English and Hanyu-Pinyin, the author shows that there is a similarity between German and Hanyu-Pinyin in terms of pronunciation and spelling. The relations set up and observed in this article are those between vowel letters and their names, between the names of vowel letter and their sounds in words, between the sounds of vowel letters and their written forms in words. The conclusion at the end may to a certain extent change the generally received claim. Key words: phoneme, grapheme, orthography Resume: En Chine, dans le milieu de l’enseignement des langues etrangeres, nombreux sont les chercheurs qui disent que l’allemand est plus difficile a apprendre que l’anglais, mais peu d’entre eux essaient de trouver les choses « faciles » dans cette langue « difficile ». Nous essayerons dans le present article de trouver des choses plus faciles a maitriser en allemand qu’en anglais, en faisant des comparaisons entre le Pinyin et ces deux langues. Ces comparaisons portent principalement sur trois relations : relation entre les voyelles et leur nom; relation entre le nom des voyelles et leur prononciation dans un mot ; relation entre la prononciation des voyelles dans un mot et l’epellation de ce mot. Le but de cette recherche est de changer en quelques sortes le prejuge qui dit que « l’allemand est plus difficile que l’anglais de tous les points de vue». Mots-Cles: phoneme, grapheme, orthographe
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".