An Investigation of the Values and Attitudes of Teachers Toward Teacher Accountability in China and Canada
Bibliographic record
Abstract
This paper presents a comparative study of teacher accountability between teachers in China and Canada.The investigation examined data using cultural and social differences toward accountability as a guide.A questionnaire developed was used to measure teacher dispositions toward internal (professional) accountability and external (bureaucratic) accountability and to determine if there were differences between teachers in the two countries, China and Canada.T-tests, analysis of variance (ANOVA), and other measures of central tendencies were used to analyze data in the Statistical Package for Social Sciences (SPSS) Version 20.Canadian teacher scores (n = 169)for external accountability (M = 4.55; SD = 0.44) were higher than teacher scores for internal accountability (M = 3.81; SD = 0.60).No differences between urban and rural areas were observed among Canadian teachers.Results showed that the means of external accountability (M = 4.13; SD = 0.59) were higher than those of internal accountability (M = 3.59; SD = 0.50) among Chinese teachers (n = 284).Canadian teacher scores were higher than Chinese teacher on both measures; the results for both external and internal accountability were found to be statistically significant.The differences suggest that further study and analyses are necessary to determine the cultural and educational implications of these differences.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".