Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
Abstract Since the 1990s there has been an increasing interest in knowledge, knowledge management, and the knowledge economy due to recognition of its economic value. Processes of globalization and developments in information and communications technologies have triggered transformations in the ways in which knowledge is shared, produced, and used to the extent that the 21st century was forecasted to be the knowledge century. Organizational learning has also been accepted as critical for organizational performance. A key question that has emerged is how knowledge can be “captured” by organizations. This focus on knowledge and learning demands an engagement with what knowledge means, where it comes from, and how it is affected by and used in different contexts. An inclusive definition is to say that knowledge is acquired theoretical, practical, embodied, and intuitive understandings of a situation. Knowledge is also located socially, geographically, organizationally, and it is specialized; so it is important to examine knowledge in less abstract terms. The specific case engaged with in this article is knowledge in hazardous industry and its role in industrial disaster prevention. In hazardous industries such as oil and gas production, learning and expertise are identified as critical ingredients for disaster prevention. Conversely, a lack of expertise or failure to learn has been implicated in disaster causation. The knowledge needs for major accident risk management are unique. Trial-and-error learning is dangerously inefficient because disasters must be prevented before they occur. The temporal, geographical, and social scale of decisions in complex sociotechnical systems means that this cannot only be a question of an individual’s expertise, but major accident risk management requires that knowledge is shared across a much larger group of people. Put another way, in this context knowledge needs to be collective. Incident reporting systems are a common solution, and organizations and industries as a whole put substantial effort into gathering information about past small failures and their causes in an attempt to learn how to prevent more serious events. However, these systems often fall short of their stated goals. This is because knowledge is not collective by virtue of being collected and stored. Rather, collective knowing is done in the context of social groups and it relies on processes of sensemaking.
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.
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 | 0,009 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,005 |
| 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 découle