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.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".