Definitional transparency

What do you actually mean by that?

Important concepts mean different things to different people. Deriverso brings a quantitative framework to that qualitative problem — derive a measurable, traceable definition for any concept and see exactly where your understanding aligns or diverges from others.

Why this matters

Every statistic bundles hidden choices — what gets counted, what gets excluded, and how components are weighted. Those choices shape policy, headlines, and public understanding, but they're rarely examined and often obscured.

Deriverso unbundles those choices. Build your own definition of a concept, connect it to real data sources, weight each component based on your confidence in it, and document your reasoning at every step.

The goal isn't to replace official statistics. It's to build a traceable definition you can share to remove ambiguity — and revisit when the ground shifts beneath it.

Under the hood

Deriverso treats definitions as first-class objects — structured, versioned, and comparable.

Weighted composition

Every measure carries an explicit weight reflecting your confidence in its relevance. Change a weight, watch the result shift.

Live data integrations

Connect measures to external APIs and data sources. Values refresh automatically, keeping your definition grounded in current data.

Transparent rationale

Every link between a topic and a measure includes a rationale field — a place to explain why this data point belongs in your definition.

Comparable definitions

Because every definition is structured the same way, you can compare yours to someone else's and see exactly where your assumptions differ.

An example: Unemployment

The Bureau of Labor Statistics reports unemployment at 4.0%. But that only counts people with no job who actively searched in the last 4 weeks — working just 1 hour per week counts as "employed." What if your definition is broader?

1 Define what goes into your number

The official rate leaves out people you think should count. Choosing what goes into your formula is the most fundamental decision.

Equation unemployment = weighted_average(
unemployment_rate,
underemployment_rate,
workforce_participation_gap
)

The official rate uses just one input. Yours uses three.

2 Connect each variable to a data source

Pick sources that stay current, swap one that lags for another that updates monthly, or exclude a source whose methodology you disagree with.

Measure Value Weight
Unemployment rate
4.0% 1
Underemployment rate
7.5% 1
Workforce participation gap
3.8% 1
Computed 5.1%

All weights equal — every measure counts the same. Already higher than the official 4.0%.

3 Weight each measure and explain why

Not every source deserves equal influence. Weight each measure based on your confidence in it — how reliable it is, how directly it captures what you care about — and document why.

Measure Value Weight
Unemployment rate The headline number — a starting point, but it excludes millions
4.0% 0.30
Underemployment rate Part-time workers who need full-time hours, plus people who stopped looking
7.5% 0.45
Workforce participation gap Working-age adults who've left the labor force — invisible to the numbers above
3.8% 0.25
Computed 5.5%

Same sources, different weights — and every choice has a reason attached to it.

Learn exactly how the math works →