People, Places and Things: Leveraging Insights from Distributed Cognition Theory to Enhance the User-Centered Design of Meteorological Information Systems
Bibliographic record
Abstract
There are many challenges in developing information systems to support information intensive collaborative work such as weather forecasting. The Australian Bureau of Meteorology has instituted the forecast streamlining and enhancement project (FSEP) for its next generation of meteorological information systems (MetIS) and significantly, has recognized the critical importance of grounding new MetIS in a thorough understanding of the weather forecasting process. This poses a major challenge for researchers due to the forecasters' very busy 24/7 deadline-driven working environment and from the fact that critical information requirements arise from the situated, embodied and distributed nature of cognitive interactions between forecasters. \nThis paper explores the utility of distributed cognition (Dcog) theory as one approach to overcome these research challenges and generate insights for the design of the Bureau's next generation of MetIS. At the theoretical level, Dcog theory allows for the capture and validation of design insights through observing cognitive behavior viewed as a system of individuals interacting within their material environment. At the methodological level, the data collection techniques deployed captures the complex socio-technical nature of forecasters' information sharing without interrupting their work. This paper highlights the utility of Dcog theory in sensitizing designers to the cognitive implications of changes to information systems and/or work processes and how the use of Dcog can empower user centered design methodologies.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".