Bibliographic record
Abstract
Purpose In The Invisible Substrate of Information Science, a landmark article about the discipline of information science, Marcia J. Bates wrote that “…we are always looking for the red thread of information in the social texture of people's lives” (1999a, p. 1048). To sharpen our understanding of information science and to elaborate Bates' idea, the work at hand answers the question: Just what does the red thread of information entail? Design/methodology/approach Through a close reading of Bates' oeuvre and by applying concepts from the reference literature of information science, nine composite entities that qualify as the red thread of information are identified, elaborated, and related to existing concepts in the information science literature. In the spirit of a scientist–poet (White, 1999), several playful metaphors related to the color red are employed. Findings Bates' red thread of information entails: terms, genres, literatures, classification systems, scholarly communication, information retrieval, information experience, information institutions, and information policy. This same constellation of phenomena can be found in resonant visions of information science, namely, domain analysis (Hjørland, 2002), ethnography of infrastructure (Star, 1999), and social epistemology (Shera, 1968). Research limitations/implications With the vital vermilion filament in clear view, newcomers can more easily engage the material, conceptual, and social machinery of information science, and specialists are reminded of what constitutes information science as a whole. Future researchers and scientist–poets may wish to supplement the nine composite entities with additional, emergent information phenomena. Originality/value Though the explication of information science that follows is relatively orthodox and time-bound, the paper offers an imaginative, accessible, yet technically precise way of understanding the field.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.062 |
| Scholarly communication | 0.030 | 0.042 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".