Implications of Markup on the Description of Software Patterns
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The reliance on past experience is crucial to the future of software engineering. There are a number of avenues for articulating experiential knowledge, including patterns. It is the responsibility of a pattern author to ensure that the pattern is expressed in a manner that satisfies the reason for its existence. This chapter is concerned with the suitability of a pattern description for consumption by both humans and machines. For that, a pattern description model (PDM), and a pattern stakeholder model (PSM) and a pattern quality model (PQM) as necessary input to the PDM, are proposed. The relationships between these conceptual models are highlighted. The PDM advocates the use of descriptive markup for representation and suggests the use of presentation markup for presentation of information in pattern descriptions, respectively. The separation of representation of information in a pattern description from its presentation is emphasized. The potential uses of the Semantic Web and the Social Web as mediums for publishing patterns are discussed.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it