Bibliographic record
Abstract
The last decade has seen remarkable changes in the way Web applications are developed and the services that are expected from them. The desire to control and manage the size and complexity of Web applications has led to a systematic approach for creating them that is known as Web engineering (Ginige & Murugesan, 2001). A focus on the "essence" rather than "accidents" is crucial to any engineering (McConnell, 1999). The engineering environment of Web applications is in a constant state of technological and social flux. New implementation languages, variations in user agents, demands for new services, and user classes from different cultural backgrounds and age groups, are faced by the Web engineers on a regular basis. For sustainability and evolvability of Web applications, it is critical that they be based upon domain, time- and technology-independent bodies of knowledge. One such invariant is the set of principles that forms the foundation of Web engineering.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".