People, Places and Things: Leveraging Insights from Distributed Cognition Theory to Enhance the User-Centered Design of Meteorological Information Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it