Week 9 — Sociology-in-Action Workshop · "Reading the Global Gap"

Course: Introduction to Sociology (SOC 1) · Silver Oak University (fictional sample) · Prof. Adeyemi
Objective: Objective 5 — global stratification; reading cross-national data · SLO B (reason from evidence; correlation vs. causation) & SLO A (apply theory)
Worth 50 points · Sociology Workshops group = 15% of the grade · Workshop 9
Mode this week: data interpretation (some weeks you observe and reflect; this week you read a real cross-national indicator). 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. This is a data week: you'll read two real figures from authoritative global sources, run the read-the-data scaffold, do a correlation-vs-causation drill, and 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 inequality isn't only within societies (Week 7) but between them — global stratification. The data beat of the week is to look at the global rich–poor gap and read it carefully: what a cross-national number actually measures, what it shows, and — the load-bearing rule — what it does not show. We'll use two of the discipline's most authoritative sources: the World Bank and Our World in Data.

The guiding question: How big is the global gap, how do we measure it — and where is the line between "these things move together" and "one causes the other"?

A theme runs through this workshop: a statistic is only as good as the definition behind it. You'll see this immediately, because the world's main poverty yardstick was redrawn in 2025.

Part 2 — The Figures (read these — they are pre-stated and verified live)

Two real, authoritative cross-national facts, each linked. Open the links and confirm the figures and years yourself — that is the whole skill.

Figure A — Extreme poverty and the line that measures it.
- The World Bank's International Poverty Line (the global "extreme poverty" threshold the UN tracks) was raised from $2.15 to $3.00 a day in June 2025 (measured in 2021 international dollars).
- Under the new $3-a-day line, the World Bank estimates that about 817 million people were living in extreme poverty in 2024. (Under the old $2.15 line, the 2024 figure had been about 692 million — the line moved by about $0.85, adding roughly 125 million people to the count.)
- Crucially, this rise does not mean the world got poorer — updated data showed the world's poorest were, if anything, slightly better off; the counted number rose because the threshold rose.
- Over the longer run, the share of the world in extreme poverty fell over a generation from about one in two or three people to about one in ten — "nine out of ten people today fall above this extreme definition of poverty."
🔗 Source — Our World in Data, "$3 a day: A new poverty line has shifted the World Bank's data on extreme poverty" (Aug 11, 2025): https://ourworldindata.org/new-international-poverty-line-3-dollars-per-day
🔗 Data home — Our World in Data, "Poverty": https://ourworldindata.org/poverty
(Verified live for this build: the $2.15 → $3/day change, June 2025; the ~817 million figure for 2024; the ~125 million increase; the "one in ten / nine in ten" framing — all read directly on the Our World in Data article above.)

Figure B — Wealth and life expectancy across countries (the correlation drill).
- On Our World in Data's interactive "Life expectancy vs. GDP per capita" chart, each dot is a country: GDP per capita on one axis, life expectancy at birth on the other.
- The pattern is one of the most reliable in social science: richer countries tend to have higher life expectancy — a strong positive correlation. The cloud of dots slopes upward, then flattens at high incomes (more GDP buys less extra life expectancy once a country is already rich).
🔗 Chart — Our World in Data, "Life expectancy vs. GDP per capita": https://ourworldindata.org/grapher/life-expectancy-vs-gdp-per-capita
(Verified live for this build: the chart resolves and shows the upward-sloping cross-national relationship; read the exact year and hover specific countries on the page.)

Why these figures, and not a single tidy number? Because the point of the week is that global figures shift with their definitions and that strong correlations are not causes. Figure A teaches you to ask which line, which year, which source. Figure B teaches you to ask correlation or causation? Confirm both on the linked pages — and if a page has updated since this was written, use the number you see on the page, with its year.

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

Work the four questions for Figure A (extreme poverty) and then for Figure B (life expectancy vs. GDP). This is the read-the-data move.

Part 4 — Correlation-vs-Causation Drill (Figure B)

Across countries, wealth and life expectancy rise together — a strong correlation. A friend concludes: "So if we just made poor countries richer, people there would automatically live much longer." Answer in a sentence or two each:

1. Is "richer countries live longer" a correlation or a proven causation? How can you tell from a scatter chart of dots?
2. Give one reverse-direction possibility — a way that better health could raise income, rather than income raising health.
3. Name one third variable that could raise both a country's income and its life expectancy (think public health, sanitation, schooling).
4. The curve flattens at high income. What does that flattening suggest about the claim that "more GDP always means much longer life"?

Part 5 — Analysis Questions (tie it to the week)

Answer in a sentence or two each:
1. Figure A: the extreme-poverty count rose in 2025 even though the world did not get poorer. Explain how that can be true — and what it teaches about trusting any single statistic.
2. Apply a theory: pick modernization OR dependency/world-systems theory and use it to interpret one of the figures (e.g., what would each predict about how the global gap changes over time?). One short paragraph; be fair to the theory.
3. Why not just GDP per capita? Using Figure B, explain why development is measured by income and health and schooling (the HDI idea) rather than income alone.

Part 6 — 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 share of the world lives in extreme poverty, and what is the international poverty line? Also explain why richer countries have higher life expectancy."
2. Check everything it says against the linked sources and this week's ideas:
   - Stale or fabricated statistic? Did it quote the old "$2.15 a day" line, or a poverty share/number it can't source? The line was raised to $3 a day in June 2025. Look up any figure it gives at Our World in Data / the World Bank and confirm the line, the year, and the source. If you can't find it, treat it as unverified — and say so.
   - Correlation as causation? Did it say (or imply) that making countries richer causes longer life, full stop? Catch the missing reverse-direction and third-variable caveats.
   - Overgeneralization / stereotyping? Did it talk about whole nations or regions in a sweeping, stereotyped way ("people there just don't…"), or treat a country average as true of every individual?
   - Theory mix-up (bonus): if it explains the theories, did it credit core/semi-periphery/periphery to "dependency theory" instead of Wallerstein's world-systems theory?
3. Write 3–4 sentences reporting what the AI got right and at least one thing you had to correct, verify, or flag — a stale poverty line, an unverifiable number, a correlation dressed up as causation, an overgeneralization, or a misattributed theory. Quote the AI's exact words for the slip you caught, and say how you verified the correct version at the source.

The habit all term: the tool drafts, you judge. A chatbot will confidently recite a poverty figure that's a year or a definition out of date, or leap from "richer is healthier" to "growth causes health." Catching it is the point.

Part 7 — What to Submit

Submit a single document (or text entry) with: your completed read-the-data scaffold (Part 3), your correlation-vs-causation drill (Part 4), your Part 5 analysis answers, and your Part 6 AI-critique paragraph (with the AI's quoted slip and how you verified the correct figure at the source). Due Sunday, Nov 1, 11:59 p.m. (50 points).

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

Every figure below was verified live against the linked Our World in Data pages at build time (Our World in Data, "$3 a day…," Aug 11, 2025; the "Life expectancy vs. GDP per capita" grapher). Data portals update; if a figure on the page differs at grading time, accept the student's correctly-sourced current number with its year.

Verified figures (the load-bearing facts):
- International Poverty Line raised from $2.15 to $3.00/day in June 2025 (2021 international dollars). ✔
- About 817 million people in extreme poverty in 2024 under the $3/day line; about 692 million under the old $2.15 line; the gap is roughly 125 million, driven by the higher line, not a poorer world. ✔
- Long-run share in extreme poverty fell over a generation from "one in two or three" to about "one in ten" (nine in ten now above the line). ✔
- "Life expectancy vs. GDP per capita" shows a strong positive cross-national correlation that flattens at high income. ✔

Part 3 scaffold — model:
- Figure A — What is measured? The share (and number) of people whose income/consumption is below the International Poverty Line (now $3/day, 2021 int-$). Population/period: all people worldwide, for a given year (e.g., 2024). Shows: roughly 817 million people below the line in 2024 (~1 in 10 of the world). Does NOT show: poverty just above the line (the $3 line is extremely low — most rich countries use thresholds ~10× higher), nor why people are poor, nor that the world got poorer when the count rose. Correlation or causation? It's a descriptive prevalence, not a causal claim — and the jump in 2024 was a definition change, not a real worsening.
- Figure B — What is measured? Each dot = a country's life expectancy at birth vs. GDP per capita (price-adjusted). Population/period: countries, for a given recent year. Shows: a strong positive correlation — richer countries tend to live longer. Does NOT show: that adding GDP causes longer life; nor anything about individuals (a country average isn't a personal guarantee); nor within-country inequality. Correlation or causation? Correlation — reverse direction and third variables are unaddressed, and the curve flattens.

Part 4 drill — model: (1) Correlation — a scatter of dots showing an upward trend is an association; it can't, by itself, show direction or rule out other causes. (2) Reverse direction: healthier, longer-lived populations can work more years and more productively, raising national income (health → wealth). (3) Third variable: clean water/sanitation, vaccination, or schooling each raise life expectancy and support higher income — lifting both without one causing the other. (4) The flattening shows diminishing returns: beyond a moderate income, extra GDP adds little life expectancy, so "more GDP always means much longer life" is false at the top end — other factors dominate.

Part 5 — model: (1) The count rose because the measuring line rose (to $3/day) while incomes among the poorest actually edged up — a vivid lesson that a statistic is only as good as its definition, year, and source. (2) Either theory, fairly applied: a modernization read might predict the gap narrows as more nations industrialize and "take off"; a dependency/world-systems read might predict the gap persists because peripheral nations' position in the global economy caps their gains — accept a fair, accurate paragraph. (3) Figure B shows income tracks but doesn't equal wellbeing; a country can have rising GDP with lagging health or schooling, so development is measured as income and life expectancy and schooling (the HDI), and even those are averages that hide internal inequality.

Part 6 (AI-critique): full credit for a specific catch — most commonly the AI quoting the stale "$2.15" line or an unsourced poverty share, leaping from correlation to causation on wealth↔life-expectancy, overgeneralizing about a region, or misattributing the world-systems zones to dependency theory. Full credit requires the AI's quoted words for the slip and a description of how the student verified the correct figure (with year + source) on the Our World in Data / World Bank page.

Grading rubric — 50 points

Quality gate (self-checked): every figure in this workshop was verified live against the linked Our World in Data pages at build time — the $2.15 → $3.00/day poverty-line change (June 2025), the ~817 million people in extreme poverty in 2024 under the $3 line (vs. ~692 million under $2.15; ~125 million difference), the long-run "one-in-two-or-three → one-in-ten" framing, and the strong positive, flattening correlation on the "Life expectancy vs. GDP per capita" chart. No figure is asserted that wasn't read on the source page, and students are explicitly instructed to re-confirm the line, year, and number themselves (and to use the page's current figure if it has updated). The workshop's entire spine is correlation ≠ causation: Figure B is framed as a correlation throughout, the drill forces reverse-direction and third-variable reasoning, and the AI-critique targets exactly the slip of dressing the wealth–life-expectancy correlation up as causation (plus stale statistics and the world-systems attribution trap). No correlation is presented as causation anywhere in this file. The theorists named (Wallerstein, by reference; Rostow elsewhere in the week) are factual.