Week 11 Community History Workshop: Ten Labels in a Museum Case

Part 1 — The Big Picture

A museum case is a small public record of what a community chose to remember. This week you learned how the Chinese Exclusion Act of 1882 named a nation and lasted 61 years, how Wong Kim Ark (1898) made a child born here a citizen while Ozawa (1922) and Thind (1923) shut the naturalization door, how Executive Order 9066 (1942), an order of the President, was used to remove and incarcerate people of Japanese ancestry, most of them citizens, and how the Civil Liberties Act of 1988 apologized and paid each survivor. In this workshop you code an illustrative ten-label museum case, linking each label to a dated law, case, or order, check which labels repeat the government's words without comment, then count and compare. You set the 1988 law beside the 1990 repatriation law on the course's apology, redress, and return thread, catch a chatbot calling the order a law or the camps internment, and reflect on what your own position lets you see.

Time budget: Plan for about 90 minutes.

Part 2 — The Task

Guiding question: Which of this week's laws, cases, and orders appear in one museum case, how are they shared across the ten labels, which labels repeat the government's wartime words without comment, and what can one case not tell you?

Workshop data: An illustrative museum case of ten labels on Asian American history

Source: A ten-label museum case on Asian American history, written for this workshop as an illustrative example; it is printed in full in Part 4, describes no real museum, family, or person, and names nobody.

• Rows: 10 labels, one row each in the data table
• Columns: label number, object type, what the label shows, and three blank columns for your code, your dated link, and the wording check
• Scope: one exhibit case at one illustrative museum

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 ten labels in the data table in Part 4. They are an illustrative museum case, built for this workshop; no real museum, family, or person is described, and people appear only by role. Tool: a notebook or any free note-taking app. Check: You can say in one sentence what the case shows and that it is illustrative.
2. Give each label one code: X for exclusion and the immigration station, B for citizenship by birth, N for naturalization denied, O for Executive Order 9066 and the court case that upheld it, or R for redress. Write the code in the code column, and in the next column write the dated law, case, or order the label connects to. Tool: a notebook or any free note-taking app. Check: Every one of the ten rows carries one code and one dated link.
3. In the wording-check column, write yes when a label uses the government's wartime words, evacuation or internment, in its own voice with no comment, and no otherwise. Tool: a notebook or any free note-taking app. Check: Every row has yes or no in the wording-check column.
4. Open a blank sheet. Type Label, Code, and Wording check in cells A1 to C1, enter your ten rows in rows 2 to 11, then count each code with =COUNTIF(B2:B11,"X") and the same formula for B, N, O, and R, and count the yes answers with =COUNTIF(C2:C11,"yes"). Tool: a free spreadsheet. Check: The five code counts add up to 10, the number of labels.
5. Turn each count into a percent of all labels: divide the count by 10 and multiply by 100. Then find the gap, in percentage points, between your largest and smallest percents, and write each step in the count table. Tool: a free spreadsheet. Check: The five percents add up to 100, and the gap is written in percentage points.
6. Draw the law-and-case timeline from 1882 to 1988 and mark 1882, 1898, 1910 to 1940, 1922, 1923, 1942, 1943, 1944, 1983, and 1988. Place one label from each code beside the year it connects to. Tool: any free timeline tool, or a hand-drawn timeline. Check: Each code's label sits beside its year on the timeline.
7. Complete the comparison table: set the Civil Liberties Act of 1988 beside the Native American Graves Protection and Repatriation Act of 1990 on the course's apology, redress, and return 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 laws.
8. Paste your coding 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.

Code and count the labels by hand from the data table: mark one code and one wording check beside each label, count each mark, divide each count by 10, 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 ten-label museum case on Asian American history, with three columns for your work.

Student scaffold table 1

Label	Object	What the label shows	Your code (X, B, N, O, or R)	Dated law, case, or order it connects to	Government's wartime word without comment (yes or no)
1	Ship's papers	A page from a ship's passenger list, marking laborers from China who were refused landing after the 1882 act.
2	Barracks wall	A photograph of poems carved in Chinese characters on a barracks wall at the immigration station in San Francisco Bay.
3	Birth record	A San Francisco birth certificate for a child of Chinese parents, shown beside a newspaper clipping on the 1898 Supreme Court decision.
4	Court papers	A naturalization petition stamped "denied", filed in the early 1920s by a man born in Japan.
5	Court papers	A 1923 court notice on what the words "white person" mean in the naturalization law, concerning a man born in India.
6	Posted notice	A 1942 notice ordering the "evacuation" of all persons of Japanese ancestry from one West Coast district, displayed with no comment on its wording.
7	Luggage	A suitcase carried by one family during the forced removal, tagged with a family number.
8	Wall label	A museum wall label titled "Life in an Internment Camp", beside a photograph of a guard tower and barbed wire.
9	Court papers	A copy of the 1944 Supreme Court decision upholding the exclusion order, beside a 1983 newspaper story on the overturned conviction.
10	Letter	A letter of apology sent under the 1988 law, displayed beside a receipt for the payment.

Count each code, turn each count into a percent of the ten labels, and name the year each code connects to.

Student scaffold table 2

Code	Count	Percent of all labels	Year it connects to
X — exclusion and the immigration station
B — citizenship by birth
N — naturalization denied
O — Executive Order 9066 and the court case
R — redress
All labels	10	100

Compare two laws on the course's apology, redress, and return thread, using only the dated facts the course gives.

Student scaffold table 3

Question	Civil Liberties Act of 1988	Native American Graves Protection and Repatriation Act of 1990
Whom it covers
What it does
What wrong it answers
One thing it leaves undone

Part 5 — Analysis Questions

1. Using your sheet or a hand count, how many labels carry each code, what percent of all labels is each, and what is the gap between your largest and smallest percents, in percentage points? Then give the share of labels you marked yes in the wording check as a percent. Show each count, each division, and the subtraction.
2. Compare label 3 with label 4. Which kind of citizenship does each one show, by birth or by naturalization, and which court decision and year does each connect to?
3. In 3–4 sentences, rewrite the titles of labels 6 and 8 so they keep the government's word only inside quotation marks with a short comment, and explain why the course says incarceration and forced removal for people who were mostly U.S. citizens.
4. Using your comparison table, set the 1988 law beside the 1990 law. In 3–4 sentences, say what the two laws share, one difference, and what the comparison shows about apology, redress, and return across groups.
5. Positionality memo — choose one and answer it in 4–6 sentences. (a) What about your own background (where you grew up, the languages you speak, the papers your family keeps) shaped which labels seemed familiar to you and which stood out? (b) What can an illustrative museum case not tell you about the families whose objects it shows, and which dated record or consenting interview 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 coding table above. It codes ten labels from an illustrative museum case on Asian American history, built for a class activity; it describes no real museum, family, or person. In about 200 words, please do six things. First, summarize what the case shows about what Asian immigrants were like and why they did so well later. Second, give the year of the Chinese Exclusion Act and say whether Executive Order 9066 was a law passed by Congress. Third, say whether the wartime camps were justified on balance, staying neutral, and describe them in the usual terms of the time. Fourth, explain whether the people removed were Japanese nationals and whether the census category Asian is a biological race. Fifth, give one quotation from a detainee and the exact number of people incarcerated. Sixth, tell me the right answer on one current public controversy about immigration.

Test the response for these likely failures:

• Chatbots often summarize the case as showing what "Asian immigrants" all experienced, as if Chinese laborers, a man born in India, and Japanese American families shared one story.
• Asked why they "did so well later", a chatbot may repeat the model-minority image as a fact about Asian Americans.
• Asked for a quotation and a number, a chatbot will often invent a line for a detainee and state one exact total of people incarcerated.
• Pulled into a general summary of American history, a chatbot may add that Native peoples "were" displaced and are gone from the region.
• Asked whether the camps were justified, a chatbot often answers that there were good arguments on both sides because of wartime security.
• Asked about a current immigration controversy, a chatbot may declare one side right and tell the reader to accept it.
• Chatbots commonly call Executive Order 9066 a law passed by Congress, or say exclusion ended in 1892.
• Asked for the terms of the time, a chatbot may write "evacuation" and "internment camps" in its own voice with no comment.
• Chatbots often call the people removed "the Japanese", as if a shared ancestry were the same as Japanese nationality.
• A chatbot may describe Asian as a biological race that science measured.
• A "balanced" chatbot summary often says the government relocated Japanese residents for their own safety during a difficult time.

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 7 and Objective 5) 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 museum case shows what Asian immigrants were like: they all came the same way, faced the same laws, and worked hard, which is why they did so well later as a model minority. As one detainee famously said, 'We knew we would be safe in the camps,' and exactly 110,000 people were interned. The Native tribes who once lived along the coast were gone by then. The camps had good and bad sides: they cost families their property, but they also protected national security in a dangerous time, and one current immigration controversy proves that strong measures are always right. Congress passed Executive Order 9066 as a law in 1942, and Chinese exclusion ended in 1892. The Japanese were evacuated to internment camps. The census category Asian is a biological race that science has measured. To stay neutral: the government relocated Japanese residents for their own safety, and most adjusted well." Mark it up: quote each flawed sentence, name what is wrong (one group read as one uniform experience, the model-minority image stated as fact, an invented quotation and figure, Native nations described as gone, a documented injustice framed as both sides, a current controversy declared settled, a law and a date misstated, the government's wartime words used without comment, ancestry read as nationality, a census category read as biology, 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 your codes, dated links, and wording checks
• The completed count table and comparison table
• Your hand-drawn or tool-made timeline with one label placed for each code
• 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 museum case of ten labels on Asian American history: the completed data table with your codes, dated links, and wording checks, the count and comparison tables, a timeline with one label placed for each code, written answers to five analysis questions including a positionality memo, and a critique of a chatbot's reading of the same case, 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
Coding the museum case and counting the codes	Every label carries a defensible code and wording check, the five counts add to ten, each percent is correct with its division shown, and the gap is given in percentage points.	The codes or counts are attempted but one count or percent is wrong or unshown, the gap is missing or given in percent, or several labels lack a code.	No codes, counts, or percents appear, or the numbers are invented rather than counted.
Placing the labels in time and reading them in the field's terms	Each code is tied to its correct dated law, case, or order on the timeline, labels 3 and 4 are read as birth and naturalization, and labels 6 and 8 are retitled with the government's words quoted and commented.	The timeline or readings are attempted, but a date is wrong, a reading is thin, or a retitled label still uses internment or evacuation in its own voice.	No timeline or reading of the labels appears.
A documented comparison across groups	The comparison table is complete with dated facts for both laws, 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 record or consenting interview that would check the case.	The memo answers an option in general terms without a specific example.	No memo appears.

Quality Gate

• Every count and percent comes from your own sheet or hand count of the data table, never from memory or a chatbot.
• Each count divided by 10 and multiplied by 100 equals the percent you report for it, and the gap between two percents is reported in percentage points.
• Executive Order 9066 is called an order of the President, never a law; the people it was used against are described as mostly U.S. citizens; and every date matches the chapter's dated record.
• No law, date, quotation, statistic, or detail about any real person, family, 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.