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Record W27932991 · doi:10.17705/1cais.01214

Web Engineering: An Assessment of Empirical Research

2003· article· en· W27932991 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCommunications of the Association for Information Systems · 2003
Typearticle
Languageen
FieldComputer Science
TopicWeb Applications and Data Management
Canadian institutionsTD Bank Group
Fundersnot available
KeywordsWeb engineeringPopularityWeb modelingWeb standardsWorld Wide WebComputer scienceWeb developmentWeb designWeb intelligenceProcess (computing)Web application securityEngineeringThe InternetPolitical science

Abstract

fetched live from OpenAlex

Web engineering is the process used to create high-quality Web-based systems and applications that deliver a complex array of content and functionality to a broad population of end-users. As Web Engineering continues to grow in popularity with practitioners and academics alike, so far, there hasn't been any assessment of its accumulated body of knowledge in terms of academic research. Because Web engineering was established as a new discipline some five years ago, it is perhaps time to take stock of the efforts made in this field. Using the Web Engineering Process Model developed by Pressman, this paper organizes and map progress made so far. The results suggest a significant need for theory-based research in Web Engineering. The paper discusses some of the managerial and research implications of the findings.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.991
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.106
GPT teacher head0.423
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it