Bibliographic record
Abstract
The reliance on the knowledge garnered from past experience can be crucial for solving problems that occur in any development (Pólya, 1945). A pattern (Buschmann, Henney, & Schmidt, 2007) is a type of conceptually reusable knowledge that has been found useful in various domains of interest (Rising, 2000). For novices, patterns have served as means of guidance; for experts, they have served as means of reference. There are a number of viewpoints of a pattern, and views emanating from these viewpoints (Kamthan, 2010). The interest in this article is to formulate an understanding of a pattern from the perspective of knowledge management. This understanding can, in turn, be useful for communicating a pattern to both humans and machines in a number of ways including publishing a pattern, disseminating a pattern, and using a pattern. The rest of the article is organized as follows. First, the background and related work necessary for subsequent discussion is outlined. Then, relevant stakeholders of a pattern are identified and, based on a process for knowledge creation and transfer that originated in industrial engineering, a knowledge management model for a pattern is proposed. Next, challenges and directions for future research are outlined. Finally, concluding remarks are given.Request access from your librarian to read this chapter's full text.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".