Investigating serendipity: How it unfolds and what may influence it
Bibliographic record
Abstract
Serendipity is not an easy word to define. Its meaning has been stretched to apply to experiences ranging from the mundane to the exceptional. Serendipity, however, is consistently associated with unexpected and positive personal, scholarly, scientific, organizational, and societal events and discoveries. Diverse serendipitous experiences share a conceptual space; therefore, what lessons can we draw from an exploration of how serendipity unfolds and what may influence it? This article describes an investigation of work‐related serendipity. Twelve professionals and academics from a variety of fields were interviewed. The core of the semi‐structured interviews focused on participants' own work‐related experiences that could be recalled and discussed in depth. This research validated and augmented prior research while consolidating previous models of serendipity into a single model of the process of serendipity, consisting of: Trigger, Connection, Follow‐up, and Valuable Outcome, and an Unexpected Thread that runs through 1 or more of the first 4 elements. Together, the elements influence the Perception of Serendipity. Furthermore, this research identified what factors relating to the individual and their environment may facilitate the main elements of serendipity and further influence its perception.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".