Adapting to uncertain and evolving enterprise requirements: The case of business-driven business intelligence
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
Information systems today are expected to function in an increasingly dynamic world with many uncertainties. System development is seldom a linear progression from well-defined, fully-specified requirements to finished products that fully meet the initial requirements. More likely, there are ongoing cycles of exploration, design and implementation, taking into account evolving needs and capabilities, as well as lessons from earlier cycles. Existing requirements modeling and analysis techniques largely presume application settings that are stable and predictable. Can these techniques be used to support analysis in the new dynamic environment? Scenarios from the recent surge in demand for business intelligence capabilities in enterprises provide an interesting setting for examining organizational and IT responses to the challenges of high uncertainty and rapid change. In this paper, we apply existing requirements modeling techniques to these scenarios in order to uncover their inadequacies, and to identify research challenges.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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