Bipartisanship
The text of this post is on X; follow the source link to read it.
Oct 10, 2026 · Posts on X (state legislators) · policy discussion; bipartisanship; International Affairs · Source
1.5×
the State Senate Democrat rate of credit claiming
25% of 204 statements claim credit (50 statements); the average for State Senate Democrats is 16%. Ranks 71st of 329, where 1st is the highest rate.
An AI model reads each of Lam’s posts on X and marks whether it claims credit for an accomplishment. The rate is the share of statements marked; it is compared with the average among State Senate Democrats and ranked against the same group.
At a glance
6 measures against State Senate Democrats. 0% of Lam’s statements contain a personal attack, in the bottom quarter of State Senate Democrats (average 2.4%). 4.9% of Lam’s statements use bipartisan language, in the top quarter of State Senate Democrats (average 3.4%).
The measures on which Lam sits furthest from the middle of State Senate Democrats, each against the same group, with the CSIP score when there is one. Every number is explained in its own section below.
Credit claiming
25%
State Senate Democrats average 16% · ranks 71st of 329
Extreme labels
1.0%
State Senate Democrats average 0.8% · ranks 73rd of 329
Personal attacks
0%
State Senate Democrats average 2.4% · ranks 248th of 329
Bipartisanship
4.9%
State Senate Democrats average 3.4% · ranks 82nd of 329
Itemized, 2026
$167.0K
State Senate Democrats’ median $55.5K · ranks 176th of 746
CSIP ideology
−0.60
Left of 0, the centre of the scale · ± 0.03
Peer group: State Senate Democrats. Rank 1 is the highest value in the group; the thick bar is Lam, the thin grey bar the peer average. The headline is the measure on which Lam is most unusual among peers (Methods, §08).
What Lam says
Lam attacks opponents personally less than 75% of State Senate Democrats. Personal attacks in 0% of 204 statements, against 2.4% for the average State Senate Democrat; 248th of 329.
An AI model reads each of Lam’s 204 statements in the MD 2025–26 session (204 posts on X) and marks the kinds of statement it contains: a personal attack, criticism of a policy, discussion of policy, bipartisanship, credit claiming, and extreme labels. A statement can carry several. Every rate is compared with the average for State Senate Democrats and ranked among them.
Kinds of statement
Share of statements carrying each label, the MD 2025–26 session
0%80%
Lam
Peers
Rank
Policy discussion84 statements
41%
43%
181st of 329
Credit claiming50 statements
25%
16%
71st of 329
Policy criticism15 statements
7%
10%
176th of 329
Bipartisanship10 statements
5%
3%
82nd of 329
Personal attacks0 statements
0%
2%
248th of 329
Show the data
| Row | Lam | Peers |
|---|---|---|
| Policy discussion | 41% | 43% |
| Credit claiming | 25% | 16% |
| Policy criticism | 7% | 10% |
| Bipartisanship | 5% | 3% |
| Personal attacks | 0% | 2% |
204
statements classified
204 posts on X.
Show the data
| Part | Value |
|---|---|
| posts on X | 204 |
How to read the bars
The thick bar is Lam; the thin grey bar is the average State Senate Democrats. A longer thick bar means Lam does this more than peers. Rank 1 is the highest rate in the group.
Extreme labels
Lam used extreme labels in 1.0% of statements, above the peer average. 2 statements, most often aimed at authoritarianism, fascism; peers 0.8%.
- “extreme”1
- “fascist”1
Who gets the labels
Targets named alongside an extreme label, by number of statements
- authoritarianism, fascism1
- gerrymanders1
What an extreme label is
Calls a politician, a party or a policy “far left”, “far right”, “MAGA”, “extreme”, “radical”, “woke”, “fascist” or “communist”. The target is who or what the label was aimed at. The lists show the five most used labels and targets; the toggle shows the rest.
Month by month
Share of statements per month, the MD 2025–26 session
Show the data
| Column | Statements | Personal attacks | Policy criticism | Policy discussion | Bipartisanship | Credit claiming | Extreme labels |
|---|---|---|---|---|---|---|---|
| Jan ’25 | 7 | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) |
| Feb ’25 | 5 | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) |
| Mar ’25 | 3 | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) |
| Apr ’25 | 16 | 0.0% | 0.0% | 56.3% | 6.3% | 50.0% | 0.0% |
| May ’25 | 10 | 0.0% | 0.0% | 10.0% | 0.0% | 0.0% | 0.0% |
| Jun ’25 | 22 | 0.0% | 0.0% | 36.4% | 0.0% | 22.7% | 0.0% |
| Jul ’25 | 5 | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) |
| Aug ’25 | 13 | 0.0% | 15.4% | 46.2% | 7.7% | 7.7% | 7.7% |
| Sep ’25 | 12 | 0.0% | 8.3% | 25.0% | 8.3% | 25.0% | 0.0% |
| Oct ’25 | 4 | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) |
| Nov ’25 | 1 | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) |
| Dec ’25 | 0 | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) |
| Jan ’26 | 9 | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) |
| Feb ’26 | 31 | 0.0% | 12.9% | 48.4% | 3.2% | 35.5% | 0.0% |
| Mar ’26 | 11 | 0.0% | 0.0% | 18.2% | 0.0% | 27.3% | 9.1% |
| Apr ’26 | 9 | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) |
| May ’26 | 2 | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) |
| Jun ’26 | 3 | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) |
| Jul ’26 | 5 | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) | — (fewer than 10 statements) |
| Aug ’26 | 11 | 0.0% | 9.1% | 90.9% | 0.0% | 27.3% | 0.0% |
| Sep ’26 | 13 | 0.0% | 0.0% | 30.8% | 0.0% | 15.4% | 0.0% |
| Oct ’26 | 12 | 0.0% | 0.0% | 58.3% | 8.3% | 8.3% | 0.0% |
How to read the strips
Each row is one kind of statement; each cell is a month, shaded against that row’s own peak (darker = closer to the peak), with the peak and the latest value written at the right. The top row counts statements. The strip covers every month of the MD 2025–26 session, the same span as the rates above, including months with no statements. A month with fewer than 10 statements is hatched, not shaded: one or two posts would swing its rate to 0% or 100%. 12 months are hatched here; 1 of them have no statements.
In Lam’s words
Real statements from the classifier’s example set, with the labels it assigned
Credit claiming
The text of this post is on X; follow the source link to read it.
Oct 9, 2026 · Posts on X (state legislators) · policy discussion; credit claiming; Government Operations and Politics; Civil Rights and Liberties, Minority Issues · Source
Extreme label
The text of this post is on X; follow the source link to read it.
Mar 30, 2026 · Posts on X (state legislators) · credit claiming; extreme label “fascist” aimed at authoritarianism, fascism · Source
Examples by category
Up to five recent statements in each category
Overtly questions a named person’s character, integrity or loyalty, or calls them names. Criticising a record or a policy does not count.
No statements in this category in this window.
All 204 statements
Newest first, the MD 2025–26 session
The full feed
Five statements at a time, newest first; filters apply to the statements loaded so far. Export saves the statements that match. Sources: 204 posts on X.
Policy
Government Operations and Politics is what Lam talks about most: 33% of policy statements, 2.3 times the peer share. Talk and legislation agree at 0.56 on a 0–1 scale (State Senate Democrats average 0.56; 123rd of 228).
Each policy statement is tagged by the AI model with one or more of 25 policy areas from the Congressional Research Service list, so the shares say what Lam talks about. The bills Lam sponsors and cosponsors carry the same areas, so the two can be compared.
What Lam talks about
Share of policy statements by area, the MD 2025–26 session
0%39%
Lam
Peers
Government Operations and Politics28 statements
33%
14%
Health14 statements
17%
17%
Immigration11 statements
13%
4%
Civil Rights and Liberties, Minority Issues10 statements
12%
8%
Crime and Law Enforcement8 statements
10%
8%
Show the data
| Row | Lam | Peers |
|---|---|---|
| Government Operations and Politics | 33% | 14% |
| Health | 17% | 17% |
| Immigration | 13% | 4% |
| Civil Rights and Liberties, Minority Issues | 12% | 8% |
| Crime and Law Enforcement | 10% | 8% |
How to read the bars
The thick bar is the share of Lam’s policy statements in an area; the thin grey bar is the average share for State Senate Democrats. A statement counts once in each of its areas, so shares can add to more than 100%. The five largest areas are shown; the toggle shows the rest.
Talks about · legislates on
Left: share of statements by policy area. Right: share of bills. Areas in order of their share of statements; both sides run from 0 to 39%. Grey ticks are the State Senate Democrats averages.
Show the data
| Area | Talks about | State Senate Democrats, talks about | Legislates on | State Senate Democrats, legislates on |
|---|---|---|---|---|
| Government Operations and Politics | 33% | 14% | 9% | 8% |
| Health | 17% | 17% | 36% | 15% |
| Immigration | 13% | 4% | 3% | 1% |
| Civil Rights and Liberties, Minority Issues | 12% | 8% | 5% | 5% |
| Crime and Law Enforcement | 10% | 8% | 3% | 9% |
0.56
talk-to-bills agreement, 0 to 1
State Senate Democrats average 0.56. Ranks 123rd of 228.
What agreement means, and what it doesn’t
Agreement is 1 minus the distance between the two distributions (Jensen–Shannon): 1 means Lam legislates on exactly the areas Lam talks about, in the same proportions; 0 means no overlap. It does not say whether the bills match the positions in the statements, only the subjects. Bills Lam sponsored count fully, cosponsored bills a quarter. Shown with at least 20 policy statements and 5 bills.
Ideology
Lam sits at −0.60 on the CSIP scale, left of 0, more conservative than 58% of State Senate Democrats. Approximate range −0.63 to −0.57, which understates the uncertainty; from 1,439 donors and 1,171 roll calls.
CSIP, the lab’s Common-Space Ideal Point, places every official, federal and state, on one left–right scale, estimated from roll-call votes, who gives money to whom, and the text of statements. Negative is left of 0, positive right of it; 0 is the centre of the scale, not a definition of “moderate”.
−0.60
CSIP ideal point
Approximate range −0.63 to −0.57. From 1,439 donors and 1,171 roll calls. Run 2026-10-25T215945. Blue is left of 0, red right of 0.
The outlined dot is Lam, coloured by its side of 0 (blue left, red right, deeper further out); the bar through it is the approximate range (±1.96 standard errors), which understates the uncertainty; the grey ticks below are the medians of Lam’s groups. 0 is the centre of the scale, not “moderate” by definition.
What this means, and what it doesn’t
The score summarises how an official votes, who funds them and how they talk, relative to everyone else. It does not grade positions as good or bad, and two officials with the same score can differ on any single issue. The ± range is 1.96 standard errors, which understates the uncertainty: scores whose ranges overlap may not differ at all, and even scores whose ranges do not overlap may not differ.
Where Lam sits
Every official in each group, on the CSIP scale
State Senate Democrats825 · median −0.7
Maryland Senate47 · median −0.6
All State Senate1,909 · median +0.1
Show the data
| Group | Officials | Median | Share left of Lam |
|---|---|---|---|
| State Senate Democrats | 825 | −0.7 | 58% |
| Maryland Senate | 47 | −0.6 | 49% |
| All State Senate | 1909 | +0.1 | 25% |
How to read the strips
Every small dot is one official on the same scale, coloured by its own score (blue left of 0, red right of 0, grey at 0, deeper further out) and spread up and down only to keep dots apart; where a group is crowded the dots overlap, and darker patches mean more officials. The large outlined dot is Lam in every row; the rule is each group’s median. The table under the figure gives the share of each group to the left of Lam.
Money
Lam reported $167.0K in the 2026 cycle, 3.0 times the State Senate Democrats’ median of $55.5K. State filings report no small-donation total. 87% of itemized individual money came from Maryland.
Campaign money from state campaign-finance filings, as aggregates only; the lab never shows an individual donor. Campaigns must report by name every person who gives them more than $200 in a cycle (“itemized”); smaller gifts are reported only as one total (“unitemized”), so donor counts, donor states, employers and occupations describe itemized donors only. State filings report no small-gift total, so these figures add up itemized contributions only.
$167.0K
itemized in the 2026 cycle
From 558 itemized donors. State Senate Democrats’ median $55.5K.
Where the money came from
Each square is one percent of itemized contributions in the 2026 cycle.
- Individuals (itemized)83 squares$138.8K
- PACs and other committees17 squares$28.2K
Show the data
| Source | Amount | Share |
|---|---|---|
| Individuals (itemized) | $138.8K | 83.1% |
| PACs and other committees | $28.2K | 16.9% |
What this means, and what it doesn’t
Totals say how much a campaign took in, not how it was spent, and they rise sharply when a seat is up for election; compare cycles with that in mind.
Receipts by cycle
Itemized contributions, 2020 to 2026. The grey line is the State Senate Democrats’ median for 2026.
Show the data
| Item | Value |
|---|---|
| 2020 cycle | $27.2K |
| 2022 cycle | $119.7K |
| 2024 cycle | $65.5K |
| 2026 cycle | $167.0K |
Top donor states, 2026
Itemized contributions by donor state. 87% came from Maryland.
- Maryland$120.1K
- Georgia$6,000
- District of Columbia$3,604
- Ohio$2,500
- Virginia$2,400
Small donations
State filings report no total for small gifts.
Not reported in state filings.
Where itemized donors live
Individuals who gave more than $200, by donor state
Show the data
| State | Value |
|---|---|
| Maryland | $120.1K |
| Georgia | $6,000 |
| District of Columbia | $3,604 |
| Ohio | $2,500 |
| Virginia | $2,400 |
| Pennsylvania | $2,150 |
| Texas | $500 |
| California | $180 |
| New York | $110 |
| Florida | $110 |
| Oregon | $50 |
| Massachusetts | $45 |
| Rhode Island | $25 |
| New Mexico | $25 |
| New Hampshire | $10 |
| Alaska | No data |
| Maine | No data |
| Vermont | No data |
| Washington | No data |
| Idaho | No data |
| Montana | No data |
| North Dakota | No data |
| Minnesota | No data |
| Illinois | No data |
| Wisconsin | No data |
| Michigan | No data |
| Nevada | No data |
| Wyoming | No data |
| South Dakota | No data |
| Iowa | No data |
| Indiana | No data |
| New Jersey | No data |
| Connecticut | No data |
| Utah | No data |
| Colorado | No data |
| Nebraska | No data |
| Missouri | No data |
| Kentucky | No data |
| West Virginia | No data |
| Delaware | No data |
| Arizona | No data |
| Kansas | No data |
| Arkansas | No data |
| Tennessee | No data |
| North Carolina | No data |
| South Carolina | No data |
| Oklahoma | No data |
| Louisiana | No data |
| Mississippi | No data |
| Alabama | No data |
| Hawaii | No data |
558
itemized donors
People who gave more than $200
87%
from Maryland
Of itemized individual dollars
How to read the map
Every state the same size. Darker states gave more itemized money; hatched states had no itemized donors; the outlined state is Maryland. Gifts of $200 or less carry no address and are left out.
Top donor occupations
Itemized, 2026 cycle.
No occupations reported.
Top committees
PAC and committee contributions, 2026 cycle.
- ANNE ARUNDEL FIRE PAC$3,000
- IBEW PAC VOLUNTARY FUND$2,500
- US ACUTE CARE SOLUTIONS PAC$2,500
- ACTIVE BALLOT CLUB LOCAL 27 EDUCATIONAL FUND PAC$2,000
- HOSPITAL ASSOCIATION PAC, MARYLAND$2,000
Top employers
Itemized, 2026 cycle.
No employers reported.
Aggregates only. Individual donors are never named.
Votes
Lam voted with the majority of Maryland Senate Democrats on 98.9% of party-line votes and missed 3.2% of 2,121 roll calls. Maryland Senate on average: 94.9% party unity, 93.6% attendance. Across all roll calls, Lam voted against the majority of Maryland Senate Democrats 17 times.
2,121 roll calls Lam could vote on in the MD 2025–26 session, compared with Maryland Senate. Attendance is the share with a vote recorded. Party unity counts only party-line roll calls, where most Democrats and most Republicans voted on opposite sides, and is the share of those where Lam voted with the majority of Maryland Senate Democrats. The count of votes against that majority covers every roll call where the majority took a side.
17
votes against the majority of Maryland Senate Democrats
Party unity 98.9%, Maryland Senate average 94.9%, ranks 15th of 46. 2,121 roll calls in the MD 2025–26 session.
Attendance and party unity
The thick bar is Lam, the thin grey bar the Maryland Senate average. The scale runs from 80% to 100% to show the gap.
80%100%
Lam
Peers
Rank
Attendance
96.7%
93.6%
23rd of 46
Party unity
98.9%
94.9%
15th of 46
Show the data
| Row | Lam | Peers |
|---|---|---|
| Attendance | 96.7% | 93.6% |
| Party unity | 98.9% | 94.9% |
What this means, and what it doesn’t
A missed roll call has many causes, from illness to campaigning, so attendance says how often, not why. A vote against the majority can be on procedure as well as substance, and many fall on roll calls where both parties split; the key votes below show the ones that decided something.
Key votes
25 roll calls the lab flagged as key in the MD 2025–26 session
| Party split (yea–nay) | Why it’s key | |||||
|---|---|---|---|---|---|---|
| Aug 4, 2026 | HB 2100 Election Districts - General Assembly and Representatives in Congress Suspended Rule 24 to allow two readings of a bill in the same day | Yea | With majority | pass | D 31–0 · R 0–13Democrats 31 yea, 0 nay; Republicans 0 yea, 13 nay | became law · much discussed |
| Aug 4, 2026 | HB 2100 Election Districts - General Assembly and Representatives in Congress Floor Amendment Salling /763427/1 | Nay | With majority | fail | D 1–29 · R 13–0Democrats 1 yea, 29 nay; Republicans 13 yea, 0 nay | became law · much discussed |
| Aug 4, 2026 | HB 2100 Election Districts - General Assembly and Representatives in Congress Floor Amendment West /943226/1 | Nay | With majority | fail | D 0–30 · R 13–0Democrats 0 yea, 30 nay; Republicans 13 yea, 0 nay | became law · much discussed |
| Aug 4, 2026 | Yea | With majority | pass | D 30–0 · R 0–13Democrats 30 yea, 0 nay; Republicans 0 yea, 13 nay | became law · much discussed | |
| Aug 4, 2026 | HB 2100 Election Districts - General Assembly and Representatives in Congress Floor Amendment Ready /393120/1 | Nay | With majority | fail | D 0–30 · R 13–0Democrats 0 yea, 30 nay; Republicans 13 yea, 0 nay | became law · much discussed |
How key votes are chosen
Automatically: a final-passage or override vote where the measure became law, the margin was under 10%, at least 10% of either party voted against its majority, or the bill was among the 25 most mentioned in officials’ own statements. Up to 25 per window, most recent first; five are shown. Bars show yea on the left, nay on the right, per party.
Votes most often with
The colleagues who voted the same way as Lam most often
- Mary WashingtonD1,865 shared votes100%
- Joanne BensonD1,263 shared votes99%
- Jim RosapepeD1,959 shared votes99%
- Brian FeldmanD2,028 shared votes99%
- Will SmithD1,982 shared votes99%
Votes least often with
The colleagues who voted the same way as Lam least often
- Johnny MautzR2,032 shared votes76%
- Justin ReadyR2,039 shared votes76%
- Jason GallionR2,038 shared votes76%
- Steve HersheyR1,897 shared votes76%
- Paul CordermanR2,033 shared votes77%
How to read the agreement lists
The share of roll calls both members voted on where they voted the same way, among colleagues sharing at least 50 such votes. High agreement with members of the other party is common on routine votes.
Full voting record
MD 2025–26 session, loads in batches
Legislation
Lam sponsored 116 bills in the MD 2025–26 session; 37 became law. Cosponsored 0 others. 54% of sponsored bills got out of committee (Maryland Senate average 60%; 31st of 47).
The bills Lam sponsored and cosponsored in the MD 2025–26 session, from the official bill records, and how far each got: introduced, referred to or acted on in committee, passed one chamber, passed both, became law. A bill counts at every stage it has reached, so the blocks only shrink to the right.
Bills by stage
Counts for the MD 2025–26 session
Show the data
| Role | Introduced | In committee | Passed one chamber | Passed both chambers | Became law |
|---|---|---|---|---|---|
| Sponsored | 116 | 116 | 63 | 41 | 37 |
| Cosponsored | 0 | 0 | 0 | 0 | 0 |
How to read the blocks
Ink blocks are bills Lam sponsored, ochre blocks bills Lam cosponsored; the figure above each block is the count. Most bills in any legislature never leave committee, so a short staircase is the norm.
54%
of sponsored bills got out of committee
Maryland Senate average 60%. Ranks 31st of 47.
Cosponsorship partners
Colleagues who most often cosponsor with Lam, by number of shared bills in the MD 2025–26 session; the square shows their party.
- Dawn Gile29
- Pam Beidle21
- Shelly Hettleman18
- Ben Kramer18
- Sara Love17
What this means, and what it doesn’t
The score is the share of sponsored bills that a committee reported out or a chamber passed, with at least 5 sponsored bills, ranked within the peer group. Members of the majority party and committee chairs move more bills, so the rank reflects position in the chamber as well as effort. Cosponsorship partners are the five colleagues who share the most of Lam’s bills; the toggle shows more.
Bill list
Sponsored and cosponsored, with status and policy area; five are shown, the toggle shows the rest
| SB 988 | Judicial Facilities - Stops, Detentions, and Arrests - Limitations | Sponsor | Feb 17, 2026 | In committee | Crime and Law Enforcement(model) | Hearing 3/12 at 1:00 p.m. Mar 6, 2026 |
| SB 962 | January 6th Insurrectionists - Prohibition on Service in Certain Government Roles | Sponsor | Feb 13, 2026 | In committee | Government Operations and Politics(model) | Hearing 3/17 at 1:00 p.m. Mar 9, 2026 |
| SB 764 | Education - Minimum Wage for Education Support Professionals - Report on Estimated Cost | Sponsor | Feb 6, 2026 | Passed one chamber | Labor and Employment(model) | Hearing 4/01 at 1:00 p.m. (Ways and Means) Mar 23, 2026 |
| SB 790 | Public Health Reform Act | Sponsor | Feb 6, 2026 | Passed one chamber | Health(model) | Referred Rules and Executive Nominations Apr 13, 2026 |
| SB 792 | Hospitals - Immigration Enforcement Action - Policy Requirement | Sponsor | Feb 6, 2026 | Became law | Immigration(model) | Approved by the Governor - Chapter 162 Apr 28, 2026 |
Online
A site that changed 2 times in 90 days. Lam’s official site had 1 page when last crawled on Oct 1, 2026.
The lab crawls Lam’s official and campaign websites (weekly for Congress, monthly for the states) and keeps every page’s text, so changes can be read later; it also records social accounts and describes the images Lam posts with a vision model.
Websites
- 1
pages crawled
2changes in 90 days
- 3
pages crawled
2changes in 90 days
How to read the counts
“Pages crawled” is the size of the site at the last crawl; “changes in 90 days” counts pages whose text differed from the previous crawl. Sites change for many routine reasons, such as new press releases, so a high count is not by itself notable.
Social accounts
Images Lam posted
No images from Lam’s posts have been described.
About the descriptions
Written by a vision model and may be wrong. Open the post to see the original. Five are shown; the toggle shows the rest.
State floor speeches
No transcribed state floor speeches are matched to this legislator yet.
Floor speeches
Transcribed from chamber video or captions by the lab. They are not classified for kinds of statement.
Methods and sources
How this page was made. Profile built Oct 28, 2026; site last built Oct 28, 2026. Every number on the page is computed from the sources listed here, by the same rules for every official; nothing is written by hand.
The headline
The headline is the measure on which Lam is most unusual among State Senate Democrats, chosen by one rule for every official of every party. The candidates are the rate of each kind of statement and of extreme labels, party unity, attendance, the small-donor share, the in-state share and total receipts of the latest cycle, the share of sponsored bills that got out of committee, talk-to-bills agreement, and the CSIP score’s place within the party group.
The builder ranks every measure within the same peer group the page compares it with, and writes each official’s percentile: the share of the group below the value, ties counting half, so 0.5 is the median and a value shared by many never ranks first. A measure’s score is how far that percentile sits from the median (0 in the middle, 0.5 at either end), and the highest score leads if it is above 0.25, outside the middle half of the group. Scores within 0.03 of each other are a tie, broken in this order: votes against the party majority, kinds of statement, ideology, roll calls missed, small donations, in-state money, legislation, agreement, extreme labels, receipts.
A measure counts only with at least 200 statements, 100 roll calls or 5 sponsored bills behind it and a peer group of at least 10. Two things never lead: high party unity or attendance, where most members sit, and a high rate of a kind of statement backed by fewer than 10 statements. When nothing qualifies, the headline is a neutral fact instead: statements classified, or years in office. The wording always names the direction (“in the bottom 5% of …”, “votes against the majority of …”).
Identifiers
- Profile id
- b002bdd1-0622-4462-8623-e8bb52fde504
- Open States
- ocd-person/b002bdd1-0622-4462-8623-e8bb52fde504
Bulk files with the same data for every official:
Freshness of this profile’s data
- What politicians sayOct 27, 20261 days agoMembers of Congress: tweets since 2017; press releases, newsletters and floor speeches since 2022. State legislators: tweets since May 2024. Governors: tweets since January 2025. Labels from an LLM classifier.
- Roll calls (state)Oct 21, 20267 days agoOpen States floor roll calls, 2019 on (none for AK and MO).
- Bills (state)Oct 28, 2026todayOpen States bills and sponsors, 2019 on; stages from roll calls.
- Campaign money (state)no dateState campaign-finance agencies, 2019 on; aggregates only.
- Ideology (CSIP)Oct 25, 20263 days agoCommon-Space Ideal Points (CSIP), current validated run.
- WebsitesOct 25, 20263 days agoWeekly crawls of federal sites since April 2026; monthly state crawls since June 2026.
- State floor speechesOct 21, 20267 days agoState floor speech turns (published transcripts and captions), 2020 on; not LLM-classified.
How the measures work
- What politicians say
- Every post, press release, newsletter and congressional floor speech is read by a language model and marked for personal attacks, policy criticism, policy discussion, bipartisanship and credit claiming. A statement can be in several. Rates are shares of statements in the window. The methods paper.
- Extreme labels
- A statement has an extreme label when it calls a politician, party or policy “far left”, “far right”, “MAGA”, “extreme”, “radical”, “woke”, “fascist” or “communist”. The target is who the label is aimed at.
- Policy areas and agreement
- Statements and bills share 25 Congressional Research Service policy areas (state bills are assigned by a language model). Agreement is 1 − Jensen–Shannon distance between the two distributions, shown with at least 20 policy statements and 5 bills.
- Ideology (CSIP)
- Common-Space Ideal Points place federal and state officials on one left–right scale, estimated jointly from roll calls, campaign contributions and the text of statements, with standard errors. Only a validated run is shown. The methods paper.
- Campaign money
- Campaigns report by name only donors who give more than $200 in a cycle; smaller gifts are reported as one total. Federal totals and the small-donor share come from the FEC’s financial summaries of each campaign committee. Donor counts, donor states, employers and occupations cover itemized donors only. State filings report no small-gift totals, so state figures are itemized only. The lab never publishes donor names.
- Votes and key votes
- Attendance, party unity and the count of votes against the majority of an official’s own party come from roll calls (Voteview for Congress, chamber records for the states). Key votes are final-passage or override votes that became law, were decided by under 10%, saw at least 10% of a party vote against its majority, or concerned one of the 25 bills most mentioned in officials’ statements.
- Peers and ranks
- Comparisons are with the same party, chamber and level (for example House Democrats); state legislators are compared within their state and chamber for votes. Every comparison names its group. Rank 1 is the highest value in the group; tied values share the average of their ranks, and the percentile is the share of the group below the value, ties counting half.
America’s Political Pulse · Polarization Research Lab