Impact 2020: the Million Muslim Votes Campaign Voter Turnout Report
Bibliographic record
Abstract
Despite the challenges presented by the COVID-19 pandemic, the 2020 presidential election set a record for voter turnout nationwide, significantly changed how Americans participate in voting, and resulted in 66.8% of eligible voters casting a ballot—7 percentage points over 2016. For years the Muslim American community has focused on improving and building institutional capacity to change the way Muslim Americans engaged in politics to ensure that the Muslim American narrative is at the core of the social fabric of this nation. In 2020, Emgage launched and implemented the largest Muslim mobilization program in history, the Million Muslim Votes campaign. Alongside statewide and national Muslim American civic groups, we concentrated our efforts on 12 states that made up a total of 1.5 million registered Muslim American voters. Our organizing efforts included making 1.8 million calls, sending over 3.6 million text messages and over 400,000 mailers, knocking on over 20,000 doors, holding over 50 organizing training sessions, and activating 672 volunteers nationwide.This effort contributed to 1,086,087 million (71%) registered Muslim voters casting a ballot, two percentage points over the 2016 turnout. Of the 1.5 million registered Muslim voters in 2020, 52% (779,793 million) voted early or via absentee ballots.This report takes an in-depth look at three core elements:the growth of the Muslim American electorate in the 12 states in which we organized;Muslim American voter turnout for 2016 and 2020, with a special focus on Michigan, Pennsylvania, Florida, Texas, Virginia, and Illinois;the pivotal organizing shifts necessitated by the COVID-19 pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".