Ranking by Now, Comparing with Then
Bibliographic record
Abstract
This chapter illustrates a dashboard showing the percentage of customers who are very satisfied with the products and services (“Promoters”), broken down by division and region. This dashboard helps to compare performance by time period(s), for example, this quarter versus the previous quarter. It also shows whether changes from a previous period are significant using whatever litmus test the company uses to determine statistical significance. It ranks sales for products and services, broken down by state, and compares them with a previous period or periods. In this dashboard, a viewer can select a region that interests him or her. The bars make it very easy to see just how one region compares with another. Sparklines show how each region is performing over time and any significant variations. The sorted bar chart worked better with the elements of the dashboard. Specifically, the sparklines, which provide an at-a-glance longitudinal view, would not complement the slope chart.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".