Week 7 Community History Workshop: A Lender's Log by Neighborhood

Part 1 — The Big Picture

A lender's rulebook is a quiet kind of record: it never names anyone, yet it decides who can buy a home and where. This week you read Brown v. Board of Education (1954) and Brown II (1955), the Montgomery bus boycott, the Civil Rights Act of 1964 and the Voting Rights Act of 1965, redlining, the Fair Housing Act of 1968, and the line between de jure and de facto segregation. In this workshop you work an illustrative log of sixteen home-loan applications at one lender, separate the neighborhood rating from each applicant's own credit record, and count what the rule does. You set one separate-schools rule beside another group's on the course's separate-schools thread, catch a chatbot misreading the log or the law, and reflect on what your own position lets you see.

Time budget: Plan for about 90 minutes.

Part 2 — The Task

Guiding question: When a lender decides by neighborhood rating, what happens to applicants with a strong credit record, and which of this week's concepts names what the log shows?

Workshop data: An illustrative log of sixteen home-loan applications at one lender

Source: A sixteen-row log of home-loan applications at one lender, written for this workshop as an illustrative example; it is printed in full in Part 4, describes no real lender, city, or person, and names nobody.

• Rows: 16 applications, one row each in the data table
• Columns: application number, neighborhood rating, the applicant's own credit record, the lender's decision, and one blank column for your mark
• Scope: one lender's log in one illustrative city

Part 3 — Materials & Procedure

Tools and materials

• A notebook or any free note-taking app for reading a record and for oral-history notes (identified by role and decade, never by name); a free spreadsheet for the lending table's rates and gaps and for any year-stamped figure; any free timeline tool, or a hand-drawn timeline, for the law-and-case timeline; and one approved chatbot used as a sparring partner whose claims about real people, laws, and dates the student checks against an issuing-body or primary source before trusting them
• a free spreadsheet
• any free timeline tool, or a hand-drawn timeline
• a notebook or any free note-taking app
• one approved chatbot

Ways of working

• read-the-record
• oral-history
• compare-and-connect

Procedure

1. Read the sixteen applications in the data table in Part 4. They are an illustrative log built for this workshop: no real lender, city, or person is described, and applicants appear only by application number. Tool: a notebook or any free note-taking app. Check: You can say in one sentence what the log records and that it is illustrative.
2. In the last column of the data table, write yes beside every application where the applicant had a strong credit record and the lender refused, and no beside every other row. Tool: a notebook or any free note-taking app. Check: Every one of the sixteen rows carries yes or no.
3. Open a blank sheet. Type Application, Rating, Credit, and Decision in cells A1 to D1 and enter the sixteen rows in rows 2 to 17. Count approvals for each rating with =COUNTIFS(B2:B17,"A",D2:D17,"approved") and the same formula for B, C, and D. Tool: a free spreadsheet. Check: The four approval counts add up to the total number of approved applications in the log.
4. Turn each count into an approval rate: divide approvals by applications and multiply by 100. Then find the gap between the highest and lowest rates, in percentage points, and do the same for strong-credit applicants alone in the A-rated and D-rated neighborhoods. Write each step in the count table. Tool: a free spreadsheet. Check: Each rate is a percent, and each gap is written in percentage points.
5. Draw the law-and-case timeline from the 1930s to 1968 and mark the 1930s lending maps, 1954, 1964, 1965, and 1968. Write beside 1968 what the Fair Housing Act made unlawful. Tool: any free timeline tool, or a hand-drawn timeline. Check: Five marks sit in order, and the 1968 mark names the Fair Housing Act.
6. Complete the comparison table: set Brown v. Board of Education (1954) beside the federal Indian boarding schools on the course's separate-schools thread, using only the dated facts the course gives. Tool: a notebook or any free note-taking app. Check: Every row of the comparison table is filled for both rules.
7. Paste your completed count table into one approved chatbot, then paste the Part 6 prompt. Check every claim it makes about a real person, law, or date against the chapter's dated record before trusting it, or take the equivalent route instead. Tool: one approved chatbot. Check: You have quoted at least three chatbot claims and corrected at least three by hand.

Fallback route

When to use it: Use this route if no spreadsheet or timeline tool is available.

Count the applications by hand from the data table: tally each rating's approvals, divide each count by 4, and multiply by 100. Draw the timeline by hand on paper, complete every scaffold table, analysis question, and the critique step, and submit the same items for equal credit.

This route earns the same credit as the primary route.

Part 4 — The Scaffold

The data come first: an illustrative log of sixteen home-loan applications at one lender, with a blank column for your work.

Student scaffold table 1

Application	Neighborhood rating	Applicant's own credit record	Lender's decision	Refused despite a strong credit record? (yes or no)
1	A	strong	approved
2	C	strong	refused
3	D	strong	refused
4	B	fair	refused
5	A	strong	approved
6	D	fair	refused
7	C	fair	approved
8	B	strong	approved
9	A	fair	approved
10	D	strong	refused
11	C	strong	approved
12	B	fair	approved
13	A	strong	approved
14	C	fair	refused
15	D	strong	refused
16	B	strong	approved

Count each rating's applications and approvals, then turn each count into an approval rate.

Student scaffold table 2

Neighborhood rating	Applications	Approved	Approval rate (percent)
A	4
B	4
C	4
D	4
All ratings	16

Compare two separate-schools rules on the course's cross-group thread, using only the dated facts the course gives.

Student scaffold table 3

Question	Brown v. Board of Education (1954)	Federal Indian boarding schools
Who was separated, and by whose rule
Where it sits on the separate-schools thread
What the dated record says about it
One difference that matters

Part 5 — Analysis Questions

1. Using your sheet or a hand count, what is the approval rate for each neighborhood rating, and what is the gap between the highest and lowest rates, in percentage points? Then give the approval rate for strong-credit applicants in the A-rated and in the D-rated neighborhoods. Show each count, each division, and each subtraction.
2. Look at the rows you marked yes. In which neighborhood ratings do they fall, and how does a fair-credit applicant's result in an A-rated neighborhood compare with a strong-credit applicant's result in a D-rated neighborhood? Say what that shows about the definition of redlining.
3. In 3–4 sentences, explain whether the lender's rule is individual or institutional discrimination, what long result the course would call structural inequality, and whether the separation it builds is de jure or de facto, naming what the Fair Housing Act of 1968 made unlawful.
4. Using your comparison table, set Brown v. Board of Education (1954) beside the federal Indian boarding schools. In 3–4 sentences, say what the two rules share, one difference, and what the comparison shows about separation required by a government's rule.
5. Positionality memo — choose one and answer it in 4–6 sentences. (a) What about your own background (where you grew up, whether your family rented or owned, the neighborhoods you know) shaped which rows in the log seemed ordinary to you and which stood out? (b) What can an illustrative log of sixteen applications not tell you about a real city's lending, and which dated record would you need to check it?

Part 6 — AI-Critique Moment

Choose one equal-credit critique route. Route A: ask an approved AI tool to critique your completed work, then check its response against your own evidence. Route B is the no-AI equivalent printed below.

Approved tools: Claude, Gemini, ChatGPT, Grok, Microsoft Copilot.

Prompt to paste:

I've pasted my completed count table above. It summarizes an illustrative log of sixteen home-loan applications at one lender, built for a class activity; it describes no real lender, city, or person. In about 200 words, please do seven things. First, summarize what the log shows about what Black families experienced and what they all wanted. Second, explain why lenders refused applicants in the D-rated neighborhoods, and say whether redlining was good or bad on balance, staying neutral. Third, give the year of Brown v. Board of Education, say whether it ended segregation, and give the year of the Fair Housing Act. Fourth, describe how the 1930s maps described residents, in the maps' own words, and say whether they sorted people by race, ethnicity, or nationality. Fifth, explain whether Black or African American in the census is a biological group. Sixth, compare the log with the federal Indian boarding schools and say what happened to the Native peoples whose children attended them. Seventh, give one quotation from a civil rights leader about redlining, one figure for how many families were redlined nationally, and the right answer on one current public controversy about housing or admissions.

Test the response for these likely failures:

• Chatbots often summarize the log as showing what "Black families" all experienced or all wanted, as if one story covered everyone in a neighborhood.
• A chatbot may explain the refusals by saying people in D-rated neighborhoods were less careful with money.
• Asked for a quotation and a figure, a chatbot will often invent a line for a civil rights leader or a national count of redlined families.
• Asked about the federal Indian boarding schools, chatbots often write that the tribes whose children attended them "were" or are gone.
• A chatbot asked whether redlining was good or bad often answers that it had pros and cons or was a sensible way to manage risk.
• Asked about a current controversy over housing or admissions, a chatbot may declare one side right and tell the reader to accept it.
• Chatbots commonly say Brown v. Board of Education ended segregation in 1954, or date the Fair Housing Act to 1964.
• Asked how the 1930s maps described residents, a chatbot may repeat the period's outdated terms as if they were current.
• Chatbots often say the maps sorted people by race, ethnicity, and nationality as if the three words meant one thing.
• A chatbot may describe Black or African American in the federal categories as a biological race that science measured.
• A "balanced" chatbot summary often says lenders were only protecting their investments and a few families were unlucky.

In 3–5 sentences, quote at least three things the chatbot wrote, say what it got right, and correct at least three of its errors by hand, naming for each the dated record or course definition you checked it against.

Equivalent route (no AI required)

No AI account is required for this workshop. This route assesses the same objectives (Objective 5 and Objective 2) for comparable effort, about 30 of the 90 minutes, under the same scoring criteria and the same credit. Instead of the chatbot, critique this flawed analysis, printed here in full: "This log shows what Black families all went through and all wanted: one shared story. People in the D-rated neighborhoods were refused because they were less careful with money. As one civil rights leader famously said, 'The red line on the map was the line around our lives,' and about 2 million families were redlined nationwide. Redlining had good and bad sides: it hurt some families, but it was also a sensible way for banks to manage risk. Brown v. Board of Education ended segregation in 1954, and the Fair Housing Act followed in 1964. The maps described most residents as colored families. The maps sorted people by race, which is the same thing as their ethnicity and their nationality. Black or African American in the census is a biological race that science has measured. The federal Indian boarding schools were like the D-rated neighborhoods, and the tribes whose children attended them are now gone. One current public controversy about housing or admissions has a clear right answer, and everyone should accept it. To stay neutral: lenders were only protecting their investments, and a few families were unlucky." Mark it up: quote each flawed sentence, name what is wrong (one group read as one uniform experience, a stereotype stated as fact, an invented quotation and figure, a documented injustice framed as both sides, a ruling and a date misstated, an outdated term used without comment, race read as the same as ethnicity and nationality, a census category read as biology, Native nations described as gone, a current controversy declared settled, and a neutral-sounding summary that quietly takes a side), and correct at least three errors by hand, naming the dated record or course definition behind each correction. Submit the marked-up analysis in place of the chatbot report and share link; it earns the same credit.

Part 7 — What to Submit

• The completed data table with every row marked yes or no
• The completed count table and comparison table
• Your hand-drawn or tool-made timeline with the 1930s maps, 1954, 1964, 1965, and 1968 marked
• Written answers to all five analysis questions, including your positionality memo
• The chatbot critique report with its share link, or the equivalent route's marked-up flawed analysis

Deliverable:

A short Community History Workshop report on an illustrative log of sixteen home-loan applications at one lender: the marked data table, the count and comparison tables, a timeline with the 1930s maps, 1954, 1964, 1965, and 1968 placed, written answers to five analysis questions including a positionality memo, and a critique of a chatbot's reading of the same log, or, on the equivalent route, a marked-up correction of the printed flawed analysis. Submit in any accessible file type or as a link; a recorded response may be submitted as audio or video with a transcript, or as text. Choose a written, recorded, or visual form for your answers; every form is graded by the same scoring criteria.

Rubric

The rubric below shows how each submitted part is evaluated; bands are full, partial, or none.

Workshop evaluation criteria

Criterion	Full	Partial	None
Working the log and counting the rule	Every row is marked, each rating's approval count and rate are correct with the division shown, both gaps are given in percentage points, and the strong-credit rates for the A-rated and D-rated neighborhoods are correct.	The counts or rates are attempted, but one count or rate is wrong or unshown, a gap is missing or given in percent, or several rows are unmarked.	No counts, rates, or marks appear, or the numbers are invented rather than counted.
Reading the rule in the field's terms and placing it in time	The refused strong-credit rows are read as the defining sign of redlining, the rule is named institutional discrimination with its long result as structural inequality, the separation is read as de facto, and the timeline places the 1930s maps, 1954, 1964, 1965, and 1968 correctly.	The readings or timeline are attempted, but a date is wrong, a concept is blurred, or the rule is read as one lender's prejudice.	No reading of the log or timeline appears.
A documented comparison across groups	The comparison table is complete with dated facts for both rules, and the answer names what they share, one difference, and what the comparison shows.	The table or answer is attempted but a row is empty, a fact is undated, or the comparison stops at listing.	No comparison appears.
The AI slip caught and corrected	Through the chatbot report with its share link or the equivalent route's marked-up flawed analysis, equally, at least three quoted errors are each named and corrected by hand against the dated record or a course definition.	Through either route, equally, one or two errors are corrected, or corrections lack the record or definition behind them.	Through either route, equally, no critique appears, or model output is pasted without any checking.
An honest reflection on positionality	The memo answers one of the two options in specific terms, naming what the student's own position let them see or miss, or naming the dated record that would check the log.	The memo answers an option in general terms without a specific example.	No memo appears.

Quality Gate

• Every count and rate comes from your own sheet or hand count of the data table, never from memory or a chatbot.
• Each approval count divided by applications and multiplied by 100 equals the rate you report for it, and every gap between two rates is reported in percentage points.
• Every date matches the chapter's dated record, every applicant is named only by application number, and Native nations are written about in the present tense.
• No law, date, quotation, statistic, or detail about any real person, lender, or community is invented anywhere in the submission.
• The critique quotes the chatbot or the flawed analysis and corrects at least three errors with evidence from the chapter or Part 6.