Serendipity in the sciences – exploring the boundaries
Bibliographic record
Abstract
Serendipity in the sciences is an unexpected experience prompted by valuable interaction with ideas, information, objects, or phenomena. While serendipity is often associated with the “aha” and “eureka” moments that characterize well-known scientific discoveries such as the structure of DNA, serendipity may be more accurately described as a factor across the various stages of the scientific process. For example, serendipity in the sciences includes those unexpected encounters with prior research findings that are fostered by informal knowledge sharing within and among scientific communities. Serendipity’s contribution to science is increasingly noted by scientists in formal scientific reports, by funding agencies which recognize the need to make room and provide support for serendipity in science, and is often credited with the development of fruitful scientific careers. This paper describes the process of serendipity—the pattern of the phenomenon—that will be familiar to many who have experienced it and noteworthy for those whose have not. Through examples of serendipity in the sciences, different perspectives on its role are explored and lessons drawn.
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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.017 | 0.063 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".