Week 15 — Sociology-in-Action Workshop · "Reading the World's Numbers"

Course: Introduction to Sociology (SOC 1) · Silver Oak University (fictional sample) · Prof. Adeyemi
Objective: Objective 8 — population & demographic change · SLO B (read & evaluate social data; correlation vs. causation) & SLO A (apply theory)
Worth 50 points · Sociology Workshops group = 15% of the grade · Workshop 15
Mode this week: data interpretation (some weeks instead use structured observation/reflection). No special tools — just a browser and an approved chatbot.

This is the course's signature weekly component. Every instructional week has one Sociology Workshop. Some weeks you read real social data (a chart or table from the Census, Pew, BLS, the World Bank, or Our World in Data); other weeks you observe and reflect on your own social world. Either way you'll end by catching an AI's mistakes. All external resources are links — nothing to buy or download.

Part 1 — The Big Picture

This week you learned that demography — births, deaths, and migration — quietly shapes whole societies, and that one number drives much of the story: the total fertility rate, the average number of children per woman. For most of human history that number was high. It has fallen dramatically almost everywhere — and yet, because of population momentum, the world's population is still growing for now. This workshop turns the read-the-data lens on the single most important demographic number there is.

The guiding question: What does the global fertility rate actually measure, what does its decline show — and what does it NOT tell us about WHY it fell?

Part 2 — The Data (identified, linked, and verified live)

Indicator: the global total fertility rate — the average number of children a woman would have given current age-specific birth rates.
Source: Our World in Data, "Fertility Rate" (Dattani, Rodés-Guirao & Roser), drawing on the UN World Population Prospects and the Human Fertility Database.
Link (verified live): 🔗 https://ourworldindata.org/fertility-rate

The figures, pre-stated and verified at the source:

• In 2023, the global total fertility rate was 2.3 children per woman.
• In the 1950s, it was more than twice as high — 4.9 children per woman.
  (Both figures appear in the opening of the Our World in Data "Fertility Rate" page and were verified there at build time. For broader context, the related "Population Growth" page reports the world reached about 8 billion people in 2022, with the growth rate down from a ~1960s peak — also verified live.)

You must verify it yourself. Open the link and find the "2.3 children per woman in 2023 … 4.9 in the 1950s" statement (and the "What you should know about this data" note) on the page before you use the number. Never use a figure you haven't seen at the source — that habit is the whole discipline of this course.

Part 3 — Read-the-Data Scaffold (fill this in)

Work the four questions for this indicator. This is the move you've done in every data-mode workshop.

Part 4 — Analysis Questions

Answer in a sentence or two each:
1. In your own words, what does a total fertility rate of 2.3 mean? (Is it a prediction of how many kids each woman will actually have? Is it the population total? Be precise.)
2. The global figure fell from 4.9 (1950s) to 2.3 (2023). Name one factor often associated with falling fertility (e.g., urbanization, women's education, falling child mortality, contraception). Does the decline by itself prove that factor caused the fall? Why or why not?
3. Use a perspective: how might a conflict theorist and a functionalist read very low fertility in some rich countries differently? (One answer each — no wrong perspective, just apply the lens.)
4. Explain population momentum in one or two sentences: how can the number of children per woman fall while the total population keeps rising?

Part 5 — AI-Critique Moment (required — this is the BYOAI step)

Now bring in your approved chatbot (Gemini, Claude, or ChatGPT) and be the sociologist who checks its work.

1. Ask it: "What is the current global total fertility rate and what was it in the 1950s? Why has the world's fertility rate fallen, and is the global population shrinking?"
2. Check everything it says against the source and this week's ideas:
   - Did it get the numbers right? Compare its figures to what you verified on the Our World in Data page (≈2.3 in 2023, ≈4.9 in the 1950s). Chatbots routinely state a precise but wrong number, or give an oddly exact figure with no source. If it can't be confirmed on the page, treat it as fabricated — and say so.
   - Did it slide from correlation to causation? If it says education (or income, or anything) caused the decline, flag it: those factors are correlated with falling fertility and move together (a third-variable problem) — the simple decline doesn't prove a single cause.
   - Did it overgeneralize or stereotype — e.g., claim "people in [some region] just want more children," pinning a complex pattern on a stereotype about a group?
   - Did it botch population momentum — e.g., claim that because fertility fell, the world's population must already be shrinking? (It isn't yet.)
3. Write 3–4 sentences reporting what the AI got right and at least one thing you had to correct, verify, or flag — a wrong/unverifiable number, a correlation-as-causation slip, an overgeneralization, or a momentum mistake. (If it happened to get everything right, say how you verified each claim against the source — that's the skill.)

The habit all term: the tool drafts, you judge. A chatbot will confidently invent a precise population statistic or turn a correlation into a cause — catching it is the point.

Part 6 — What to Submit

Submit a single document (or text entry) with: your completed read-the-data scaffold (Part 3), your Part 4 answers, and your Part 5 AI-critique paragraph (including the AI's claimed numbers and your verification against the source). Confirm you opened the source link and saw the figure. Due Sunday, Dec 13, 11:59 p.m. (50 points).

Instructor answer key & model responses — REMOVE BEFORE PUBLISHING TO STUDENTS

The figures are verified live at the source (Our World in Data, "Fertility Rate," drawing on the UN): global total fertility rate ≈ 2.3 children per woman in 2023, down from ≈ 4.9 in the 1950s. The key grades the reasoning — correct reading of the indicator, and a real correlation-vs-causation / momentum catch — not a single "right" phrasing.

Read-the-data scaffold (model):
- What is measured? The total fertility rate = the average number of children a woman would have if she experienced one year's age-specific birth rates across her life. It is not a prediction of completed family size, and not the total population.
- Over what population/period? The whole world; comparing the 1950s (≈4.9) to 2023 (≈2.3).
- What does it show — and not? A large, worldwide decline — births per woman more than halved. It does not tell us why it fell, or how many children today's women will ultimately have, or anything about a single country (it's a global average over very different places).
- Correlation or causation? Urbanization, women's education, falling child mortality, and contraception access all rose alongside the fertility decline and are correlated with it — but the global decline does not by itself prove any one of them caused it; these factors move together (a third-variable/confounding problem). A correlation is a clue, not a verdict.
- The momentum twist: even with fertility this low, world population is still growing because a large young generation is still entering its childbearing years (population momentum); the world reached ~8 billion in 2022.

Expected answers:
- Part 4 Q1: 2.3 = average births per woman under current age-specific rates; not a guaranteed personal family size and not a population total. Q2: any real associated factor (urbanization, education, child survival, contraception) with the correct point that the decline alone does not prove causation (correlation/third variable). Q3: any reasonable application — e.g., a functionalist might focus on how institutions (pensions, labor force, schools) adapt to fewer children; a conflict theorist might focus on whose interests low/high fertility serves, or on inequality in who can afford children — full credit for accurate use of the lens, not a "correct" verdict. Q4: momentum — a large young cohort keeps total births (and population) rising despite low per-woman fertility.
- Part 5 (AI-critique): full credit for a specific catch — most commonly the AI giving a precise-but-unverifiable number, asserting a cause for the fertility decline from a correlation, overgeneralizing/stereotyping a region, or claiming the population is already shrinking. Full credit also if the student verified each AI claim against the source and reported how.

Grading rubric — 50 points

Quality gate (self-checked): the asserted statistic — the global total fertility rate of 2.3 children per woman in 2023, down from 4.9 in the 1950s — was verified live at the source (Our World in Data, "Fertility Rate," https://ourworldindata.org/fertility-rate, drawing on the UN World Population Prospects) at build time; the ~8-billion-in-2022 context figure was likewise verified at Our World in Data's "Population Growth" page. The definition of the total fertility rate, the population-momentum concept, and the demographic framing are all accurate. The workshop explicitly trains students to treat any factor associated with the decline as a correlation, not a proven cause, and to verify any AI-supplied number at the source — the discipline's load-bearing risks. No correlation is presented as causation.