On the business value and technical challenges of adopting Web services
Bibliographic record
Abstract
Abstract This paper provides a balanced perspective of the business value and technical challenges of adopting Web services. Technology adoption is a continual challenge for both tool developers and enterprise users. Web services are a prime example of an emerging technology that is fraught with adoption issues. Part of the problem is separating marketing hype from business reality. Web services are network‐accessible interfaces to application functionality. They are built using Internet technologies such as XML and standard protocols such as SOAP. The adoption issues related to Web services are complex and multifaceted. For example, determining whether this technology is a fundamental advance, rather than something old under a new name, requires technical depth, business acumen, and considerable historical knowledge of past developments. A sample problem from the health care industry is used to illustrate some of the adoption issues. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".