<論文>日本におけるケアエコノミー研究に関する展望論文(2) ―ケアダイアモンド研究の到達点と課題―
Bibliographic record
Abstract
本稿は，市川・須原・相馬(2025)の分析結果を踏まえ，ケアエコノミーの理論的発展及び実証分析の精緻化を検討するうえで重要となるケアダイアモンドの概念整理のため，先行研究におけるケアダイアモンドの研究史における位置づけと理論及び実証分析の到達点と課題を検討した．その結果，第1にケアダイアモンドによりケアを実際に提供している多様な主体を把握することが可能であることから，国家間比較及び1か国における時系列比較と制度間比較に有効な概念であることが明らかになった．第2にケアダイアモンドのさらなる発展・精緻化には，比較する際の指標の明確化，各主体の明確化及び協力・緊張関係の把握について課題があること，また比較対象の国・社会の文脈の把握及びグローバルな視点が必要となることも明らかとなった．そしてケアダイアモンドの発展は，ケアエコノミー研究の発展との相互作用が期待できる．
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".