Amplifying chance for positive action and serendipity by design
Bibliographic record
Abstract
Abstract In recent years, there has been an increased focus on information encountering and serendipity within information behavior research and practice. Serendipity has the potential to facilitate creativity and innovation in various spheres, including in libraries, archives and museums. However, do we wait for chance to occur, or can serendipity be designed and facilitated? What are the characteristics of systems that support serendipitous discovery, and what methods can be used to study its occurrence? Extending and building on the concepts and definitions introduced at a 2016 ASIS&T Annual Meeting panel led by Erdelez, we feature in this 40‐min panel innovative work that creates opportunities for discovery within research spaces. Attendees engage through an interactive two‐part discussion and a hands‐on ideation session on impacts and guidelines for systems designed to facilitate serendipity, emphasizing sustainable, accessible researcher and user experiences. Presenters focus on the role of socio‐technical constraints and affordances to inform systems' design in a variety of research contexts, each contributing expertise in navigating particular issues in serendipity research.
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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 | 0.024 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".