Development and Validation of a Short-Form Inventory to Identify Philosophy of Education in Japan: Learning & Educator Nurturing Style (LENS)
Bibliographic record
Abstract
This paper presents the development and validation of the Learning & Educator Nurturing Style (LENS), a new inventory in Japanese designed to identify and assess educational philosophy. Based on the Philosophies Held by Instructors of Lifelong-learners (PHIL) framework, LENS was created through a rigorous back-translation process to ensure linguistic and cultural accuracy. The development process included item translation, expert review, and pilot testing to ensure conceptual clarity and cultural relevance within the Japanese context. This article details the theoretical foundations of LENS, the back-translation approach using PHIL, and the outcomes of a validation study conducted with a sample of educators. Additionally, this study explores the role of artificial intelligence (AI) in enhancing academic research, detailing how AI-assisted tools supported literature synthesis, coherence refinement, and statistical analysis while maintaining rigorous human oversight. The final instrument provides a reliable and comprehensive tool for categorizing various educational philosophies, offering valuable insights for researchers and practitioners interested in understanding and applying educational philosophy in Japan. Furthermore, this research contributes to the evolving discussion on AI-assisted methodologies, demonstrating how AI can enhance—not replace—academic inquiry.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".