Design for Maintenance: How Kms Document Linking Decisions Affect Maintenance Effort and Use
Bibliographic record
Abstract
Information system maintenance is an important aspect of information system development, especially in systems that provide dynamic content, such as Web-based systems and Knowledge Management Systems (KMS). Design for Maintenance (DFM) is an approach that argues that maintenance effort should be considered during the design of information systems in addition to the usual system design considerations. This research examines how the design of links among knowledge documents in a KMS affects both their maintenance and use. We argue that providing links among knowledge documents increases the cost of maintenance because when a document changes, the documents that link to and from that document are more likely to need changes. At the same, linking knowledge documents makes it easier to locate useful knowledge and thus increases use. We examine this tension between use and maintenance using 10 years of data from a well-established KMS. Our results indicate that as the number of links among documents increases, both maintenance effort and use for these documents increase. Our analyses suggest two DFM principles for dynamic content in practice. First, knowledge coupling (i.e., linking) to documents internal to the KMS rather than sources external to the KMS better balances maintenance effort and use. Second, designing small, knowledge cohesive documents (e.g., 250-350 words) leads to the best balance between maintenance effort and use.
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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 | 0.020 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".