Bibliographic record
Abstract
With the increasing globalization of food trade and the harmonization of food standards and food safety measures, significant changes in the international and national regulatory frameworks for food\nhave come about as well. And, although there is increasing recognition of the need to integrate and improve coordination of regulatory activities among national and international bodies, food regulation\nremains complex. Case-based teaching has been demonstrated as an effective educational tool for better\nunderstanding food regulation complexities. In this paper, the characteristics of a good case study are delineated, and a hypothesized case of attempting to import a soy milk product into Canada is looked at\nin detail. The considerations and points highlighted in this case study help portray the complicated nature of food regulation and the necessity for a multifaceted, yet systematic, approach to effectively resolving\nfood regulation issues and cases. In addition, the advantages of employing the case study method as an effective didactic tool can be clearly seen.\n食品取引が世界規模で増加し、食品に関する基準と安全性確保のための措置が一体化する状況において、世界および各国の食品規制の枠組みにも大きな変化が生じてきている。国家および国際的な組織における規制活動を統合し改良することが必要であるという認識は高まっているが、食品規制はあいかわらず複雑な状況にある。事例に基づく指導は、食品規制の複雑さをよく理解させるための効果的な教育手段であることが論証されてきている。本稿においては、良い事例研究がもつ特徴を詳述するとともに、豆乳製品をカナダに輸入しようとする場面を想定して、これを詳しく検討する。この事例研究においては、食品規制が持つ複雑さ、および、その問題点を効果的に解決するための、多面的かつ組織的な取り組み\nの必要性について考察する。さらに、事例研究という方法を効果的な説明手段として用いることの利点も明らかにする。
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".