Learning by Connecting: How Rule Networks Evolve Through Discovery of Relevance
Bibliographic record
Abstract
Learning-by-connecting, the formation of connections between lessons, is a fairly common phenomenon, but how does it evolve? We argue that learning-by-connecting unfolds as the relevance of lessons to other lessons is gradually discovered over time. The process of “relevance discovery” unfolds through a dynamic interplay between lessons and their context that provides opportunities to discover the relevance of lessons to other lessons. We develop a theoretical model in which the availability of these opportunities and their sorting in time drive the formation of connections. We explore and test our model in the context of organizational rules that we conceptualize, following rule-based learning theories, as repositories of lessons learned. Our empirical context is the formation of citation ties between clinical practice guidelines (CPGs), a type of organizational rules in healthcare, in a Canadian regional healthcare organization. We find that citation tie formation intensifies when opportunities to discover relevance become available. We also find that learning-by-connecting creates rule networks in which the formation of new ties slows down due to the sorting of opportunities in time. Our findings support our assumption that learning-by-connecting is shaped by relevance discovery. Our study extends models of rule-based learning and contributes to discussions on the formation of connections in contexts of dispersed learning and knowledge.
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How this classification was reachedexpand
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".