Day 1 · the through-line

Connotations & Code

Why no number is ever neutral

Every arrow from a lived human fact to an "insight" is a human choice. Today we turn that on the very first thing you'll touch — a plain number.

A printed numeral on parchment casting a shadow shaped like a crowd of arguing figures

What does the count say?

Our corpus: 93 YouTube comments under one CBS News video (Aug 20, 2025) about the federal ruling that blocked Texas's law putting the Ten Commandments in public-school classrooms — copied soon after with a free browser scraper.

Capitals ignored, "commandments" appears 25 times. "Constitution" appears 8.

Flat, true, countable.

So what is it telling us?

Bar chart: in 93 comments, with capitals ignored, "commandments" appears 25 times and "constitution" 8

What does the count say?

Before you read meaning into it, list what else could explain it.

A number shows a difference. What the difference means is your argument, not the number's.

Denotation and connotation

You already own the tool this lecture needs.

  • A word's denotation is what it literally points to. House, home, and residence denote the same thing: a building people live in.
  • Its connotation is everything it carries on top — warmth, money, bureaucracy, belonging.

Home and residence denote alike and connote worlds apart.

The same house painted three ways — warm cottage, notarized "residence," listing card — one denotation, three connotations

Denotation and connotation

Now the move that makes you a data humanist:

Numbers have connotation too.

"Single-mother household" denotes a category on a form. It connotes a whole contested story about blame and worth — and which story you hear depends on who's speaking.

The denotation is the surface. The connotation is where the culture lives — and where the argument is.

The same house painted three ways — warm cottage, notarized "residence," listing card — one denotation, three connotations

The myth of neutral data

Three comforting things people say about data. Each one is too simple.

  • "Data doesn't lie." — It doesn't have to lie to mislead. It can simply leave things out.
  • "The numbers speak for themselves." — They never do. Someone always speaks for them, by choosing the frame.
  • "It's just objective analysis." — Every dataset records somebody's choices about what was worth counting.
A weaver at a lamplit loom drawing threads from spools carved with faces; the cloth coming off the loom is woven in rows of tally marks

The myth of neutral data

Data carries the values, assumptions, and blind spots of whoever collected, sorted, and counted it.

That isn't a flaw you can scrub out. It's the opening lecture's diagram again: every arrow there was a choice, and so is every column here.

A dataset is a record of choices before it is a record of anything else.

A weaver at a lamplit loom drawing threads from spools carved with faces; the cloth coming off the loom is woven in rows of tally marks

Same numbers, three stories

School A — average SAT 1240
School B — average SAT 980

Watch the same denotation spin into three connotations, each quietly handing the blame to someone else:

  • Deficit story — School B's students are underperforming. (It's the kids.)
  • Systemic story — School B is under-resourced and underfunded. (It's the system.)
  • Cultural story — the SAT measures one kind of knowledge. (It's the test.)
Two stacks of graded papers on a schoolroom table, one tall and one short; tired students, a man pointing at a leaking ceiling, and a woman pointing at a thick exam booklet each blame something different

Same numbers, three stories

Nothing in the numbers tells you which story is true. They show a difference, not its cause.

And even the difference can be staged. Same two numbers, two charts — on the right, School B looks like it's collapsing.

Choosing the story is the human part — an argument about fairness, wearing the costume of arithmetic.

The same two SAT averages, 1240 and 980, drawn twice: with the axis starting at 0 the bars look close; starting at 900, School B looks like it is collapsing

Who's doing the telling?

If numbers don't tell their own story, someone does — usually someone with power over the frame.

  • Institutions define the categories — a government writes the census boxes, a university decides what "success" counts as
  • Media sets the frame — the headline, the axis a chart starts at, the context cut for space
  • Researchers choose the question — who gets sampled, and who paid for the asking
A hand holds an empty wooden picture frame up to a crowded market street; only a small slice of the scene falls inside it

Who's doing the telling?

So the reflex this course wants to build. Every time a number lands in front of you, ask:

Who collected this, why, and whose story does it tell?

A hand holds an empty wooden picture frame up to a crowded market street; only a small slice of the scene falls inside it

The missing and the uncounted

The loudest thing about a dataset is often what isn't in it.

Our 93 comments are not "the public." They're the slice of the public that posted — people online, fired up enough to type, willing to be seen arguing.

A hall of seated figures where several chairs are empty and some silhouettes are faint and half-painted — the uncounted

The missing and the uncounted

The woman from the opening lecture posted. Her neighbor — who felt just as strongly and never typed a word — is not in the file at all. Neither is anyone without the wifi, the time, or the nerve.

Absence from the data is not absence from reality.

A schematic funnel: everyone the law affects, people who saw this one video, people who chose to comment, 93 comments the scraper caught, 123 rows in your file

So: code is not neutral

Code inherits every connotation above — and adds its own, because code runs on definitions, and a definition is a decision.

Over the next weeks you decide. What counts as a "word" when you split a comment? Which words are "stopwords" worth deleting? What score makes a comment "positive"? Where do you cut one topic from the next?

A hand at a keyboard whose keys are tiny brass levers, each keypress sending golden light down a branching track

So: code is not neutral

Each choice is a small act of interpretation that looks like a neutral function.

And those choices don't merely encode a value — they argue one.

Drop every word used fewer than five times, and your code has claimed that rare words matter less. In our file, that one rule deletes 651 of 749 different words.

Code isn't only structure. It's rhetoric.

Two bars: the rule "drop every word used fewer than 5 times" keeps 98 different words and drops 651 (87%); by word uses it drops 42%. Kept: the, to, of, and, is, commandments, religion, god. Dropped: slavery, adultery, empathy, Muslim, Jewish, atheist, separation

So: code is not neutral

That's the trap and the responsibility at once: a machine looks objective precisely because it hides the choices inside it.

Read data like a text, and write code that shows its choices.

Leave the #comment that says what you chose, and why. That comment is not housekeeping. It's where the honesty shows.

↤ Lectures·Full text
← → navigate · click to advance