Information Management Practices and Methodologies in Architecting Information Systems
Bibliographic record
Abstract
This research uses content analysis to provide a comprehensive overview of current trends in Information Architecture (IA) for Information Management (IM). It clarifies the IA concept, its elements, design practices, and methodologies. Additionally, it explores the education, roles, and skillsets expected of information architects in today’s job market. This research can be used to train IA stakeholders, define information architect responsibilities, standardize terminology, and develop best practices and standards for IA design. Ultimately, this work contributes to the evolving field of IA by reducing ambiguity and offering pedagogical insights for Library and Information Studies programs. Pratiques de gestion de l'information et méthodologies dans l'architecture des systèmes d'information RésuméCette recherche utilise l’analyse de contenu pour fournir une vue d’ensemble compréhensible des tendances actuelles en Architecture de l’Information (AI) pour la Gestion de l’Information (GI). Elle clarifie les concepts de l’AI, ses éléments, ses pratiques de conception et ses méthodologies. De plus, elle explore les rôles de l’éducation et les compétences attendues pour les architectes de l’information sur le marché du travail actuel. Cette recherche peut être utilisée pour entraîner les responsables d’AI, définir les responsabilités des architectes de l’information, standardiser la terminologie, et développer les meilleures pratiques et normes pour la conception de l’AI. En définitif, ce travail contribue à l’évolution du domaine de l’AI en réduisant l'ambiguïté et en offrant des perspectives pédagogiques pour les programmes d’études en Bibliothéconomie et Sciences de l’Information. Mots-clésarchitecture de l’information; gestion de l’information; méthodologie en architecture de l'information; pratiques de l’architecture de l’information: architecte de l’information
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.
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 | 0.033 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".