A Leadership Model for Supporting the Mastery of Core Competencies for College English Learners in Application-Oriented Universities in Shanghai, China
Bibliographic record
Abstract
On basis of Synergistic Leadership (Irby et al., 2002), Situational Leadership (Hersey & Blanchard, 2008), the support systems in Framework for 21st Century Learning (P21, 2019), and related research, this research employed questionnaire surveys and interviews in four application-oriented universities (AOUs) in Shanghai, characterized by disciplines of humanities, arts, technology, and health. Findings from questionnaire surveys and interviews showed that: 1) 45 leadership factors in the context of AOUs in Shanghai may be synthesized from dimensions of stakeholders’ perceptions, leadership behaviors, and external forces; and 2) leadership styles may start at any point of directing to coaching styles, and gradually transform to supporting, and specific leadership behaviors have been provided to CE instructors or instructional leaders in determining or adapting situational leadership styles to learners’ situation in AOUs in Shanghai. Based on the findings, the model Leadership Atomium was developed for instructional leaders and instructors to support learners’ mastering CE core competencies for their better preparations for future life and work.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".