Theoretical Construction of Educational Leadership Discipline from the Perspective of Research Based Learning
Bibliographic record
Abstract
The development of research-based learning has put forward new requirements for the theoretical construction of educational leadership discipline. The theoretical construction of educational leadership discipline should be based on research-based learning, and new adjustments and innovations should be made in research methods, research paradigms, and other aspects. The current theoretical construction of educational leadership should focus on "research-based learning", attach importance to the intersection and integration with other related disciplines, and continuously enrich and develop the theory of educational leadership. This article mainly introduces the connotations of research-based learning and educational leadership, and then analyzes the impact of research-based learning on educational leadership. Finally, relevant suggestions for the theoretical construction of educational leadership discipline are proposed, hoping to provide reference and assistance.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.038 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".