Cultural Awareness in Primary School English Teaching
Bibliographic record
Abstract
The English curriculum at the stage of compulsory education is of the dual nature of instrumentality and humanity. When it comes to humanity, the task of English curriculum is to improve students’ comprehensive humanity qualities, one of which is to deepen their understanding of different cultures. Cultural awareness is a reflection of the core literacy in English learning. The cultivation of students’ cultural awareness not only contributes to students’ strengthening of national identity, enhancing of cultural self-confidence in the traditional culture of the nation, but also helps develop an inclusive attitude towards excellent foreign cultures, thereby improving their cross-cultural communication competence. Since the textbooks are regarded as core teaching materials, it is essential for textbooks to contain various cross-cultural elements to better cultivate learners’ cultural awareness. Therefore, cultural content in English textbooks is a necessary issue to be investigated. This paper took 6A Unit 8 Chinese New Year (Yilin Edition) as an example to integrate more cultural knowledge into the specific teaching design. The results in this paper reveal that teachers play four roles in primary school English teaching in terms of raising students’ cultural awareness, namely, the emotion motivator, the cognition inspirator, the behavior guide and the morality regulator.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".