An Information Governance Methodology to Tackle Digital Recordkeeping Challenges: The Convergence of Artificial Intelligence, Business Analysis and Information Architecture
Bibliographic record
Abstract
In the paper, a five-step methodology comprising 1) IM Need and Capacity Analysis; 2) Functional Analysis; 3) Process Analysis; 4) Information Architecture Development; 5) NLP Requirement Specifications and Iteration is presented. This presentation is followed by a discussion demonstrating how the methodology fulfills the Information Governance compliance requirements while promoting a better coordination of information management strategies, with both IT, security and performance measurement strategies. The methodology also lays the foundations to integrate recordkeeping automation to current recordkeeping practices based on techniques derived from research in artificial intelligence. Dans le document, une methodologie en cinq etapes comprenant 1) l'analyse des besoins et des capacites de GI; 2) Analyse fonctionnelle; 3) Analyse des processus; 4) Developpement de l'architecture de l'information; 5) Les specifications et iterations des exigences PNL sont presentees. Cette presentation est suivie d'une discussion demontrant comment la methodologie repond aux exigences de conformite de la gouvernance de l'information tout en favorisant une meilleure coordination des strategies de gestion de l'information, avec les strategies informatiques, de securite et de mesure de la performance. La methodologie jette egalement les bases pour integrer aux pratiques actuelles l'automatisation de la gestion de documents basee sur des techniques derivees de la recherche en intelligence artificielle.
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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 | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".