Notice bibliographique
Résumé
Government regulation of individual and business activity is part and parcel of modern society. But many businesses face difficulties in understanding and navigating the legal hurdles, rules, and uncertainty that come with modern regulation. Many governments in Canada have taken steps to reduce this burden by streamlining regulation and cutting unnecessary red tape. In this Commentary, I explore how regulators can continue this trend toward more efficient and effective regulation: by embracing data analytics and machine-learning tools. Big data, analytics and machine learning offer new and difficult challenges for regulators who oversee how many businesses make decisions. But regulators can also benefit from effective use of data science. Some of these benefits can be realized almost immediately by using data that the regulators already have. First, regulators can better predict who should and should not be investigated. A regulator needs to make choices about how to allocate and prioritize scarce resources. With the right data and appropriate data analytics, predictions can be made about where to best place investigation resources. Second, regulators must make choices over which cases to prosecute. Regulators should not waste resources litigating cases they are likely to lose. Instead, regulators should put resources only toward cases that they are likely to win. Regulators can turn to the data and use machine learning to predict how a court would resolve a particular problem. Moving further into the future, big data and machine learning will change the way that laws and regulations will be consumed and produced. Lawmakers will have greater ability to provide relevant information before the individual or business acts, rather than waiting to adjudicate after they have acted. Businesses will seek prior authorization for many more regulated actions. Furthermore, the time and cost for regulators to respond to the queries will fall drastically. Instead of relying primarily on vague guidelines, regulators will be able to offer more expedient and personalized responses. There are enormous benefits to regulators making decisions before individuals and business act. Advance rulings, given before investments are made, provide certain outcomes and reduce the likelihood of wasted investments. There are, of course, a number of potential barriers and issues that may arise. These include: the quality of the data, accountability and due process, the need for transparency, privacy and the reluctance to share data, the benefits of uncertainty, and the stability of social views and goals.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».