From Learning Theory to Academic Organisation: The Institutionalisation of Higher Education Teaching Assistant Position in China
Bibliographic record
Abstract
This exploratory study critically investigates the teaching assistant regulations of higher education institutions of China. On the basis of content analysis of the teaching assistant regulations of five premier universities of China this study analyses the possible discrepancies that might compromise the principles of transparency, equal opportunity and encouraging excellence as stipulated in the vision, mission, and goal of the regulations. Teacher assistants do make more than two third of the academic staff at the universities in China. Besides, China has a second largest higher education system in terms of scale in the world. Practices of sharing skills and imparting knowledge at these institutions have been intermediated by a semi-institutionalized position, called ‘teacher assistants’. It’s therefore, the informal submission of assignments without record at the PhD level questions the purpose of integrity and academic freedom of the higher education at the universities. On the basis of an instrumentalised framework guided by the dimensions of decision making and learning organization theories this study using content analysis has formulated the recommendations for the institutions while selecting and training the students as teaching assistants. A critical but logical illustration of the teaching assistant regulations has also been detailed regarding academic integrity in this study.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".