Function-based records classification systems : an exploratory study of records management practices in central banks
Bibliographic record
Abstract
Records management and archival theory recommends that records classification, as a means to identify and organize the records made or received in the course of business, should be based on an analysis of the records creators’ functions and activities and reflect them. However, the purpose of classification, the meaning of the term function, and the methodology for conducting a business analysis are not clearly explained in the relevant literature. Additionally, no studies of actual applications of the functional approach to records classification in real organizational settings exist. This dissertation addresses the question of how the concept of function and the functional approach to records classification are understood by those who are responsible for the development and implementation of records classification systems as well as by the users of such systems. In order to contribute insights that can enrich the theory and methodology of records classification, an empirical, interpretivist research design, based on an initial survey of potential study subjects and a multiple-case study research, was conducted in four selected central banks in Europe and North America. One of the selection criteria was that the organizational cultures of the case study sites had to be as heterogeneous as possible. Findings showed that the meanings of function, functional approach, and even classification are subject to various interpretations, that classification developers find functional methodologies confusing, and that users do not usually appreciate the outcomes of their efforts. Furthermore, because the approach to classification was not always consistent with the nature of the records, some of the classification systems examined did not adequately serve either records management or business-related purposes. The research also provided an explanation of the relationship between organizational culture and the understanding of both records management and business processes.
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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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".