Research on Serendipity: The Methodological Challenges of Time and Language
Bibliographic record
Abstract
We explore the words academics (N = 26) in Germanyuse to describe unexpected and useful experienceswith information. We further report on the perceptionsof a portion of the participants’ experiences during anexploratory work task and a follow-up survey designedto capture reports of serendipity several days later.Nous explorons les mots que les universitaires (N =26) utilisent en Allemagne pour décrire desexpériences inattendues et utiles lors demanipulations d’information. Par ailleurs, nousrendons compte des perceptions d’une partie desexpériences des participants au cours d’une sessionde travaux exploratoires et une enquête de suiviconçue pour enregistrer les mentions de sérendipitéplusieurs jours plus tard.
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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 | 0.051 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".