People Don’t Practice What They Preach? Inclusive Educational Policy and Exclusionary Practices for Children with Autism Spectrum Disorders in China’s Compulsory Education: A Critical Assessment
Bibliographic record
Abstract
Purpose: Autism Spectrum Disorder (ASD) is a neuro-developmental disorder characterized by behavioral deficits. In China, children with ASD find themselves critically challenged due to inadequate support and insufficient awareness among the general public. Methods: This paper illustrates the implementation of Learning in Regular Classroom Program (LRC) in China, comparing and deconstructing the special education policies in two consecutive phases in a southern metropolis in China. Results: A Critical assessment reveals 1) retrogression in guiding principles, 2) inconsistent models of disabilities, 3) consistent use of enrolment rate as Key Performance Index (KPI) and 4) non-mandatory use of language to guide implementation. Conclusion: The widening gap between the conceptualization and realization of the LRC in China is beyond question. Suggested solutions are: 1) professional projection of possible challenges for children with ASD; 2) adequate supply of resource classrooms and special education teachers appointed for the implementation of the Individual Education Plan (IEP); 3) a statutory code of practice in ASD studies.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".