Bringing Together Functional Classification and Business Process Analysis: Growing Trends in Records Management
Bibliographic record
Abstract
Drawing on our experience in developing business classifications, this communication identifies and discusses the challenges related to function-based approaches while proposing a methodology that reconciles the organizational perspective (i.e. the functional model) with the end-user perspective (i.e. the day-to-day tasks within a business process).Étayée par notre expérience dans le développement de systèmes de classification fonctionnelle, cette communication expose les défis inhérents à cette approche et propose une méthodologie qui réconcilie le point de vue organisationnel (c’est-à-dire le modèle fonctionnel) et le point de vue de l’utilisateur final (c’est-à-dire les tâches quotidiennes d'un processus d’affaires).***Full paper in the Canadian Journal of Information and Library Science***
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 | 0.062 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.022 | 0.037 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.021 | 0.042 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 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".