Special section: Narratives, facts and events in the foundations of information science
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".