APPLICABILITY OF CROSS-SECTORAL MANAGEMENT OF INFRASTRUCTURES IN MUNICIPALITIES
Bibliographic record
Abstract
老朽化が進む社会基盤施設を効率的に管理するための一方策として，複数分野の社会基盤施設を分野横断的に管理する手法がある．本研究では，社会基盤施設の分野横断的管理のメリット・デメリットを俯瞰的に言及する．また，分野横断的管理の実現を後押ししうる外部環境変化についても整理し，依然として各所管省庁の縦割り，用途変更に伴う補助金の返還義務など，制度的課題は残存しつつも，公会計の整備，PFI法改正，DXの発展などに伴い，その導入に向けた技術・制度の推進状況が向上していることを指摘する．さらに，分野横断的管理導入の効果を定量的に評価するための簡易シミュレーションを実施して，分野間の融通によるリスクファイナンスや，更新ピークの時間的分散効果による更新費の平準化の可能性を示す．
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".