Everyday serendipity as described in social media
Bibliographic record
Abstract
Abstract Serendipity has received much attention from library and information science, psychology, and computer science. Yet not much is known about serendipity in the context of everyday information behavior. In general, a key challenge in the study of serendipity is obtaining accounts of serendipitous experiences that provide insight into the phenomenon. The exploratory research reported here approaches this problem by examining naturally occurring descriptions of serendipity as found on blogs. The paper shows how these data can be collected, stored, and analyzed. We also discuss strengths of the proposed approach in comparison to the use of descriptions elicited in controlled settings for the purposes of research. Through a grounded theory approach, we develop a model of serendipity that can inform the design of information systems. The paper contributes to the LIS field by discussing an alternative data collection method for serendipity research, outlining a tentative conceptual model of serendipity, and showing the utility of this model for the analysis of everyday accounts of serendipity found on blogs.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".