A Psychological Research on Characters in Middle School Chinese Textbooks in China
Bibliographic record
Abstract
This study made a statistical study and analysis of the Chinese textbooks for six-year-system students. The results showed: (1) in terms of nationality, the number of characters of the Han nationality was 4 times as many as that of minority nationalities. 74.3 per cent of the students can’t tell the differences. In comparison, in primary school Chinese textbooks (The People’s Education Press, in 1993)[1], the progress had been made without doubt; (2) in terms of countries, the number of Chinese characters was 3.98 times as many as that of foreigners and the description of foreigners did not meet well the requirement of times development; (3) in terms of genders, characters of male were 2.4 times as many as female; (4) in terms of live environment, the proportion of the characters living in ancient was 46.7%. So the description of the contemporary and rural circumstance was not enough;(5) concerning identity and occupation, they focus on men of letters and so on, but pay little attention to ordinary people occupation. Key words: Middle School Chinese Textbooks, Students, Character’s Feature Resume: Cette etude a fait des statistiques et l’analyse de materiels chinois pour les eleves du cycle de 6 ans d’etudes . Le resultat a montre: (1) En terme de nationalite, le nombre de caracteres de la nationalite Han etait de 4 fois celui des groupes minorites. 74.3 % des eleves ne parviennt pas a dire les differences. Comme comparaison, dans les ecoles primaries, les materiels chinois (la Presse d’ Education du peuple, en 1993)[1], il est indoutable qu’ils ont connu un progres; (2) En terme de pays,le nombre de caracteres chinois etait de 3.98 fois celui des etrangers dont la description n’ont pas satisfait les demandes du developpement; (3) en terme de genre, les caracteres masculins etaient de 2.4 fois ceux des feminins; (4) En terme de l’environnement de vie, la proportion de caracteres anciens etait de 46.7%. Donc la description de la contemporaine and la circomstance rurale n’etait pas suffisante;(5) Concernant l’identite et l’occupation, ils mettent un accentt sur les hommes de letttres etc, mais mettent tres peu d’attention a l’ occupation des gens ordinaires. Mots cles: Materiels Chinois dans les Ecoles Secondaires, Eleves, caracteristiques des caracteres
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".