Influence of Acculturation in Yunnan’s Ethnic Minority College Students on Their Academic Achievement: The Moderating Role of Learning Motivation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p style="text-align:justify">This study examined the effect of Yunnan’s ethnic minority college students’ acculturation on their academic achievement under the risk of the Matthew effect. Additionally, the role played by learning motivation in the relationship between ethnic minority college students’ acculturation and academic achievement was explored. A total of 403 valid questionnaires were collected from four areas in Yunnan province, China. Consequently, the Acculturation Scale, Academic Achievement Scale, and Learning Motivation Scale were used for measurement materials. These items of scales were evaluated on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). SPSS (statistical package for the social sciences) and AMOS (analysis of a moment structures) softwares were used for data analyses. In addition, items were analyzed through item analysis, confirmatory factor analysis, reliability analysis and regression analysis. These results indicated that ethnic minority college students with low acculturation and learning motivation or high acculturation and low learning motivation can become objects of the Matthew effect. However, this study also observed that in certain students, high acculturation and high learning motivation can prevent the Matthew effect. Thus, high acculturation is crucial for improving academic achievement in ethnic minority college students. A level of high learning motivation is a powerful moderator promoting the academic achievement of students with high acculturation.</p>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it