Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it