Dashboards: From Performance Art to Decision Support
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
Abstract Interview with Neil Hoyne, Chief Measurement Strategist at Google Dashboards are a common tool for managers to monitor a company’s performance, and since the COVID-19 pandemic they have gained popularity among even broader audiences. But what is the real use of these dashboards? Is it just performance art or is it a tool that provides managers with the information they need? It may be slightly astonishing that Google employee Neil Hoyne is no fan of dashboards, but he believes they can be toxic when taken out of context. In this interview, he explains his skepticism of monitoring the same KPIs quarter after quarter and suggests different ways to make dashboards more strategically useful to companies. In his view, dashboards should inspire questions and curiosity, reflect market context and align toward specific business initiatives. He also suggests a more professional use of data and favors the scientific inquiry of the relationship between marketing measures and business outcomes.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.005 | 0.054 |
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