INSPIRED BY THE UNEXPECTED - SERENDIPITOUS LEARNING
Bibliographic record
Abstract
Inspired by the unexpected - serendipitous learning According to Dr. Allen Tough, futurist, scientist, professor at the University of Toronto, about 80% of learning is informal rather than professionally planned. Serendipitous learning might be considered as a subset of informal learning. Formal learning is experienced in an authority-based, course-oriented school, but nowadays, with the incredibly amounts of information that are available through the Web, a special kind of learning makes its presence felt, assuming eminence's learning that is discovery based. Serendipitous learning precipitates exploratory or informal learning which is less formal than objectives-driven approaches, but is still influenced by personal experience, goals and interests. Serendipity is the effect of discovering something really interesting, whilst looking for entirely something else, in other words a completely unintended but fortunate discovery. But serendipitous learning is associated with the idea that "we are more likely to be receptive to serendipitous discovery if our minds have undergone some prior training or preparation. Preparation, training and knowledge do not guarantee serendipitous discovery, but they do increase the probability of discovery. This skill is sometimes referred to as intuitive sagacity, in which seemingly disparate pieces of information undergo a process of mental incubation and are brought together by an external catalyst such as a research query ". This article aims to investigate and reflect on the exploratory hunger of the online learning fostered by the greatest serendipity engine in the history of culture - the Web - pointing out ssome powerful implications of this view.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".