Supporting Leadership Factors for the Mastery of Core Competencies for College English Learners in Application-Oriented Universities in Shanghai: A Pilot Studyore Competencies for College English Learners in Application-Oriented Universities in Shanghai: A Pilot Study
Bibliographic record
Abstract
Based on the theory of Synergistic Leadership (Irby et al., 2002), as well as the Framework for 21st Century Learning (P21, 2019) and related research, this research applied mixed methods with questionnaire surveys and interviews to propose the supporting leadership factors for the mastery of core competencies for College English (CE) learners in one of the application-oriented universities (AOUs) in Shanghai, China. The research objectives included: 1) to identify the elements of support systems desirable for supporting the mastery of core competencies for CE learners; 2) to determine the leadership factors expected to support the mastery of core competencies for CE learners in AOUs in Shanghai, China. The quantitative analysis was applied on the data from literature, as well as the questionnaire surveys with 428 learners and 19 instructors, whereas qualitative method analyzed the data from the interviews with one instructional leader and two professors in this AOU. In terms of the educational elements coded from literature, 39 supporting leadership factors were synthesized and proposed, categorized into synergistic leadership factors of stakeholders’ perception, leadership behavior, and external forces. 
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".