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Normativity by Default: When the Average Becomes the Ideal 📊📏🧍‍♂️#

“Who gets to be the center?”

“The model said you’re different.
The system heard: you’re wrong.”

Averages are meant to describe populations.
But in practice, they often become moralized standards:

  • “Below average kidney function”

  • “At-risk student”

  • “Low adherence patient”

This is normativity by default:
The transformation of statistical majority into moral benchmark.

🧠 Why It Happens#

  • Designers need defaults

  • Interfaces love thresholds

  • Institutions prefer clean lines

  • Humans crave belonging

So what was once a center point becomes a judgment line.

🧬 Clinical Example#

A 60-year-old Black woman is told her eGFR is “borderline.”

But:

  • The reference cohort is mostly younger, white, male

  • Frailty, socioeconomic stress, cumulative discrimination aren’t accounted for

  • The GFR threshold is a one-size-fits-none construct

Still, she leaves the appointment feeling abnormal.

That is normative harm.

🧭 How It Shows Up#

  • Dashboards color-coded with red/yellow/green based on cohort averages

  • Insurance denial letters citing deviation from “standard care paths”

  • Mental health algorithms trained on middle-class, Western norms

  • “Acceptable risk” defined by historical decisions, not current values

The model did not intend judgment.
But the structure delivered it.

🛠 Ukubona’s Response#

  • No fixed “normal”—always stratified, always contextual

  • Subgroup definitions declared and questioned

  • Outcomes labeled with confidence and humility, not certainty

  • Color and tone selected to guide, not dictate

We show where a user lands without implying where they should be.

🧾 Final Thought#

You are not a deviation.
You are a data point with context.

Averages are helpful.
But they are not goals.
They are not gods.

Next: Stability or Stagnation? – When Reliability Hides Decay

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