Week 11 — Sociology-in-Action Workshop · "Reading the Pay Gap"

Course: Introduction to Sociology (SOC 1) · Silver Oak University (fictional sample) · Prof. Adeyemi
Objective: Objective 6 — read the gender pay-gap data and distinguish the uncontrolled ("raw") women's-to-men's earnings ratio from a controlled estimate · SLO B (reason from evidence) & SLO A (apply theory)
Worth 50 points · Sociology Workshops group = 15% of the grade · Workshop 11
Mode this week: data interpretation (other weeks alternate with observation/reflection workshops). 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'll read real social data (a chart or table from the Census, Pew, BLS, the World Bank, or Our World in Data); other weeks you'll 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 to separate sex (biology) from gender (the social meanings a society attaches), and you met the major perspectives on gender inequality. One of the most-cited pieces of evidence about gender inequality is the gender pay gap — usually reported as the women's-to-men's earnings ratio (women's median earnings as a percentage of men's). It's also one of the most-misread statistics in public life: people stretch it to mean "women are paid X% for the exact same job" (it isn't that), or shrink it to "a myth, fully explained by choices" (it isn't that either). This workshop turns the read-the-data move on the real BLS figures — and teaches the single most important distinction for reading them: the uncontrolled ("raw") gap vs. a controlled estimate.

The guiding question: When the news reports "the gender pay gap," what is the number actually comparing — and what does it show, and not show, about why women and men earn differently?

The ground rule for this charged topic. The existence of a measured gap is documented — we report it plainly. What is genuinely debated, and what you'll weigh, is the explanation (occupational segregation, hours, the motherhood penalty, discrimination). We present the competing explanations fairly; we do not "both-sides" whether the gap exists.

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

We're reading the women's-to-men's earnings ratio from the U.S. Bureau of Labor Statistics (BLS). Open the pages yourself and find these numbers — that habit is the whole point. (All figures below were verified live at bls.gov on the build date, 2026-06-29.)

Figure A — the current quarterly ratio.
- Indicator: median usual weekly earnings of full-time wage and salary workers, women vs. men.
- Figure (verified on the BLS page): in the first quarter of 2026, women's median weekly earnings were $1,098, or 80.6% of the $1,362 median for men. (This is the raw / uncontrolled ratio.)
- Source (links only): U.S. Bureau of Labor Statistics, Usual Weekly Earnings of Wage and Salary Workers, First Quarter 2026 (news release USDL-26-0622, released April 16, 2026).
🔗 https://www.bls.gov/news.release/wkyeng.nr0.htm

Figure B — the annual benchmark.
- Indicator: women's annual-average earnings as a percentage of men's, full-time wage and salary workers.
- Figure (verified on the BLS page): for full-year 2023, women's earnings were 83.6% of men's. (Also a raw / uncontrolled ratio. See especially Table 12, which gives the ratio by year back to 1979.)
- Source (links only): U.S. Bureau of Labor Statistics, Highlights of Women's Earnings in 2023 (Report 1111, August 2024).
🔗 https://www.bls.gov/opub/reports/womens-earnings/2023/home.htm

A "raw vs. controlled" note (and a verification rule): both BLS figures above are uncontrolled ("raw") ratios — they compare the median of all full-time women to the median of all full-time men. A controlled (adjusted) estimate compares women and men after accounting for measurable factors like occupation, industry, experience, and hours; it is usually smaller than the raw gap, but in many careful studies it does not fully disappear. This workshop does not assert a specific controlled-gap figure — controlled estimates come from particular studies with particular methods, so if you want to cite one, you must read it off that study at its source. The discipline you're practicing: read the number at its source, note the year, and know whether it's raw or controlled before you use it.

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

Work the scaffold for Figure A (the 2026 Q1 ratio, 80.6%) — and answer the raw-vs-controlled question at the end. This is the what-is-measured → over-what → what-it-shows-and-not → correlation-or-causation move.

Part 4 — Analysis Questions

Answer in a sentence or two each:
1. In your own words, what does the raw women's-to-men's earnings ratio (Figure A, 80.6%) actually compare? Name one thing it does NOT show.
2. List two documented explanations for the overall gap (from: occupational segregation, hours/continuous experience, the motherhood penalty, discrimination), and for each say in a few words what it contributes.
3. A friend says, "The controlled pay gap is small, so the raw gap is fake and there's no real problem." Using the raw-vs-controlled distinction, explain why that doesn't follow.
4. Pick one perspective from this week — conflict/feminist or functionalist — and explain in two sentences how it would interpret the pay gap. (Remember: the data describe the gap; the perspectives interpret it. Keep the documented gap intact.)

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 gender pay gap right now, what causes it, and is sex the same as gender?"
2. Check everything it says against the BLS sources and this week's ideas:
   - Did it blur sex and gender? Watch for it using the two as synonyms, or calling a social role "biological." (The week's signature error.)
   - Did it invent or misdate a statistic? Verify any pay-gap percentage on the BLS pages (the raw ratio was 80.6% for 2026 Q1; 83.6% for full-year 2023). If a number isn't on the source — or is tagged to the wrong year, or doesn't say whether it's raw or controlled — treat it as fabricated, misdated, or unlabeled, and say so.
   - Did it overclaim in either direction? Catch any leap to "women are paid X% for the exact same job" (the raw ratio is all-women-vs-all-men, not identical jobs/hours) OR "the gap is a myth, fully explained by women's choices" (the controlled gap often doesn't fully vanish, and "choices" are shaped by gender norms).
   - Did it slide from a correlation to a cause? E.g., "women entering a field causes its pay to drop" — that's a correlation whose direction is unsettled (selection, the devaluation hypothesis, or a third variable).
3. Write 3–4 sentences reporting what the AI got right and at least one thing you had to correct, verify, or flag — a sex/gender mix-up, an unverifiable/misdated/unlabeled figure, an overclaim in either direction, or a correlation-as-causation slip. (If it happened to get everything right, say how you verified each claim at the source — that's the skill.)

The habit all term: the tool drafts, you judge — and you verify every number at its source. A chatbot will confidently confuse sex with gender, misdate a percentage, or overclaim the gap in one direction — catching it is the point.

Part 6 — What to Submit

Submit a single document (or text entry) with: your completed Part 3 scaffold, your Part 4 answers, and your Part 5 AI-critique paragraph. Note the source and year for any figure you reference, and say whether it's raw or controlled. Due Sunday, Nov 15, 11:59 p.m. (50 points).

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

All figures below were verified live at the source on the build date (2026-06-29). Women's-to-men's earnings ratio 80.6% in 2026 Q1 (women $1,098 vs. men $1,362): U.S. Bureau of Labor Statistics, Usual Weekly Earnings of Wage and Salary Workers, First Quarter 2026 (USDL-26-0622, released April 16, 2026), seen in the release "Highlights." Annual ratio 83.6% for 2023: U.S. Bureau of Labor Statistics, Highlights of Women's Earnings in 2023 (Report 1111), Table 12. No controlled-gap figure is asserted — students must read any adjusted estimate off the specific study at its source. Both BLS figures are uncontrolled ("raw") ratios.

Model worked scaffold (Figure A — 2026 Q1 raw ratio, 80.6%):
- What is measured? The ratio of the median usual weekly earnings of all full-time women to all full-time men. It is not a "same job, same hours" comparison — it compares two middle workers across the whole full-time workforce. "Median" = the middle value (half earn more, half less), so it isn't pulled up by a few very high earners.
- Over what population and period? All full-time wage and salary workers, first quarter of 2026 (not seasonally adjusted), from the Current Population Survey.
- What it shows / does NOT show: it documents a real overall earnings gap between full-time women and men. It does NOT compare identical jobs/hours, does NOT by itself isolate why the gap exists (occupational segregation, hours, the motherhood penalty, and discrimination all sit inside it), and a single ratio hides variation (e.g., the gap differs by age and by race/ethnicity).
- Correlation or causation? The raw gap describes a difference in outcomes; by itself it does not prove what causes it. Establishing how much is due to each factor requires further analysis (this is where controlled estimates come in).
- Raw vs. controlled check: a controlled estimate compares women and men after statistically accounting for occupation, industry, experience, and hours — it asks "same-ish job and hours, what's left?" It is usually smaller than the raw gap (because some of the raw gap reflects which jobs people hold and how many hours they work), but in many studies a residual remains that does not fully disappear.

Expected answers:
- Q1: the raw ratio compares the median earnings of all full-time women to all full-time men (not the same job hour-for-hour). It does not show the cause, or compare identical jobs/hours, or reveal within-group variation. (Full credit does not require a controlled figure — only the correct read of the raw figure.)
- Q2: any two of — occupational segregation (women and men cluster in different-paying fields); hours/continuous experience (on average, differences in hours and continuous time in the labor force); the motherhood penalty (mothers' earnings fall in ways fathers' don't); discrimination/bias (can persist after measurable factors are accounted for; part of the residual). Full credit names two and says what each contributes.
- Q3: the raw and controlled gaps measure different things — the raw gap captures the total difference (including occupational segregation and hours), while the controlled gap isolates the "unexplained" slice after accounting for measurable factors. A small controlled gap does not make the raw gap "fake"; the raw gap is a real difference in outcomes, and the controlled gap often doesn't fully vanish anyway.
- Q4: Conflict/feminist: the gap reflects a patriarchal organization of paid and unpaid work — occupational segregation and the caregiving (motherhood) track channel women into lower-paid positions; ask who benefits. Functionalist (now widely critiqued): a traditional complementary division of labor might frame some of the gap as following from differing roles — but the standard critique is that this naturalizes inequality and ignores power. Full credit names the perspective, keeps "describe vs. interpret" straight, and does not deny the documented gap.
- Part 5 (AI-critique): full credit for a specific catch — most commonly the AI blurring sex and gender, inventing/misdating/unlabeling a pay-gap figure (unverifiable at BLS, or not flagged as raw vs. controlled), overclaiming in either direction ("same job" or "a myth"), or sliding from a correlation to a cause ("women entering a field causes pay to drop"). Full credit also if the student verified each figure at BLS and reported how.

Grading rubric — 50 points

Quality gate (self-checked): the published figures asserted in this workshop — the women's-to-men's earnings ratio 80.6% (2026 Q1, women $1,098 vs. men $1,362) and 83.6% (full-year 2023) — were verified live at the U.S. Bureau of Labor Statistics (news release USDL-26-0622 and Report 1111, Table 12) on the build date; source and year are stated for each, and both are flagged as uncontrolled ("raw") ratios. No controlled-gap figure is asserted — the workshop deliberately reframes the controlled-gap point around the skill (read it from the specific study at its source) rather than stating an unverified number, per the discipline's load-bearing rule. The sex-vs-gender distinction, the raw-vs-controlled distinction, and the documented explanations (occupational segregation, hours, the motherhood penalty, discrimination) are all accurate. Sensitivity: the workshop reports the documented gap plainly (not "both-sidesed") while presenting the competing explanations evenhandedly, and it explicitly guards against overclaiming in either direction. The AI-critique targets the sex/gender confusion, fabricated/misdated/unlabeled statistics, directional overclaims, and the correlation-as-causation slip — the discipline's load-bearing AI risks. No correlation is presented as causation: the raw gap and the "women's fields pay less" finding are explicitly framed as descriptive/correlational, not causal.