Investigating serendipity: How it unfolds and what may influence it
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.024 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it