Bibliographic record
Abstract
While serendipity is generally considered a spark for innovation and new knowledge, the triggers for serendipity appear infinite and consequently information systems' support for serendipity has been difficult to realize. Research to date has tended to focus only on supplying users with unexpected triggers for serendipity (e.g., embedded links in results). We adapt a model of the serendipitous process that examines serendipity more holistically. Using previously collected data, we focus on understanding the precipitating conditions that must be present to facilitate serendipity. Results suggest that serendipity occurs during social networking and active learning, and more specifically in the act of exploratory search. Results also suggest that serendipity is not always instant -- the usefulness of triggers may not be immediately apparent and a period of incubation is sometimes necessary before recognition of the serendipitous nature of a latent trigger is attained. Implications for the design of information systems are explored and support for the incubation period is discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".