Bibliographic record
Abstract
Previous research on abortion-related crowdfunding campaigns found that they are impacted by stigma around abortion and rarely successful. This paper analyzes crowdfunding activity in the US following a leak of the Supreme Court decision in Dobbs. V. Jackson Women's Health Organization, a time period that saw increased financial support of abortion access funds. Crowdfunding campaigns that included "abort" or "abortion" and were created between May 2 and November 8, 2022 were recorded from the GoFundMe and GiveSendGo crowdfunding platforms. These campaigns were reviewed for whether they were US based and sought funding where abortion was used as a justification for support. Included campaigns were assigned a campaign recipient type: (1) Organizations providing abortion access; (2) Organizations seeking legal protection for abortion; (3) Individuals seeking abortion access; (4) Organizations seeking to reduce abortion access; and (5) Individuals with needs resulting from choosing not to access abortion. The authors also identified four types of rationale for supporting these campaigns. Following a leak of the Dobbs decision, 398 abortion-related crowdfunding campaigns in the US raised over $3.8 million from over 50,000 donations. Campaigns supporting abortion access organizations raised higher median amounts than organizations seeking to reduce abortion access. Individuals seeking abortion access raised higher median amounts than individuals who chose not to terminate a pregnancy. In a reversal from pre-Dobbs crowdfunding, abortion access campaigns tended to outperform other abortion-related campaigns. It is not clear how long-lived this change in support will be and campaigners remain vulnerable to changes in platforms' content moderation policies.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".