Special section: Narratives, facts and events in the foundations of information science
Notice bibliographique
Résumé
The humanities and social sciences are concerned with the human experience. Sciences, too, deal with actions, processes and interactions. Information systems, therefore, are concerned with events, but can operate only on objects (bits, books, “documents”)—and events are not objects. Suzanne Briet wrote that “a document is evidence in support of a fact,” but facts (like data) have no meaning absent a narrative explanation. The three papers in this section explore the role of events and facts in information organization and retrieval and are based on their authors' presentations at the 2009 ASIS&T Annual Meeting in Vancouver in a panel with the same title sponsored by the Special Interest Group/History and Foundations of Information Science (SIG/HFIS). In “From Facts to Judgments: Theorizing History for Information Science,” Ryan Shaw, a doctoral candidate at the University of California, Berkeley, discusses the past as idealized images of people, places, events and ideas. Greatly expanded access to historical information through digitization has led to projects to extract facts from such resources in order to present history succinctly in databases. Shaw discusses the limitations of approaches that lift facts from their narrative context in the historical accounts. He advocates systems that enable us to see and retrieve historical events as bundles or colligations of narratives. Thomas Dousa, in “Facts and Frameworks in Paul Otlet's and Julius Otto Kaiser's Theories of Knowledge Organization,” traces the origins of the idea that information units—or facts—can be extracted from documents and (re)organized within the frameworks of knowledge organization systems (KOSs). Otlet and Kaiser, who were both pioneers in knowledge organization in the late 19th and early 20th centuries, held nearly identical views about the analysis of documents into aggregates of facts, but key differences in their methodological and ideological outlooks resulted in vastly divergent narratives of knowledge organization and starkly different KOSs. Otlet developed a universal KOS: the UDC; Kaiser's approach was particularist, creating different narratives for specific communities — a tension that is all too familiar to contemporary practitioners. Dousa is a doctoral student at the University of Illinois, Champaign-Urbana. Finally, Michael Buckland and Michele Ramos in “Events as a Structuring Device in Biographical Mark-up and Metadata” report on the rationale for using events to structure biographical data for markup. Events are seen as arbitrarily defined actions suitably framed by the four facets of what, where, when and who. The paper summarizes the problems and solutions for each of these categories.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| 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) | 0,000 | 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 ».