Preliminary study on design and development of a journal focused crawler system using EBD methodology: Part I — Design task and environment analysis
Bibliographic record
Abstract
This paper is part one of a preliminary study on design and development of a journal focused crawler system using EBD methodology. In this paper, the authors found that the living environment of a web crawler has been widely changed and the development technology has also been greatly improved, therefore it is assumed that the focus of design and development of a web crawler system shall be different than before. Using EBD, designers can begin from recursively analysing the environment of the design task for gathering enough requirements to dig out the real intent of a design task. The question-asking techniques about the generic questions and domain questions can give designer a good practice on how to analyse the environment, it guides designer to fully consider the factors that affecting the design product. These factors may lie in each event or phase of any related lifecycle, or within any level of requirement from nature to human and built. Finally, the environment analysis will lay a foundation for later conflict identification and solution generation processes since designer have grasped the real intent of the design task to some extent.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".