An Ontology for Contextual Information System Design
Bibliographic record
Abstract
Collaborative teamwork is becoming more common in several domains including healthcare and disaster management. While collaborative teamwork can benefit from information system (IS) support, designing IS models to support collaboration is a significant challenge owing to the variations in tasks and people that must be supported, and the different contexts within which collaboration takes place. Collaborative teamwork can vary greatly because of context, which is the integration of diverse, dynamic, and heterogeneous needs for groups to achieve a specific goal. However in the literature there has been limited emphasis on how contextual underpinnings can be incorporated into IS design. This paper uses a case study of the design of a user-driven prototype disaster management IS. We used the think aloud method to capture participant thoughts while interacting with the IS prototype. The think aloud data was analyzed and used to develop an ontology of contextual considerations to support IS design.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".