A goal-oriented, business intelligence-supported decision-making methodology
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
In many enterprises and other types of organizations, decision making is both a crucial and a challenging task. Despite their importance, many decisions are still made based on experience and intuition rather than on evidence supported by rigorous approaches. Decisions are often made this way because of lack of data, unknown relationships between data and goals, conflicting goals, and poorly understood risks. This research presents a goal-oriented, business intelligence-supported methodology for decision making. The methodology, which is iterative, allows enterprises to begin with limited data, discover required data to build their models, capture stakeholders goals, and model threats, opportunities, and their impact. It also enables the aggregation of Key Performance Indicators and their integration into goal models. The tool-supported methodology and its models aim to enhance the user’s experience with common business intelligence applications. Managers can monitor the impact of decisions on the organization's goals and improve both decision models and business processes. The approach is illustrated and evaluated through a retail business scenario, from which several lessons were learned. One key lesson is that once an organization has a goal model, data can be added iteratively. The example, tool support, and lessons suggest the feasibility of the methodology.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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