Analysis for Public Discourse Based on Tweets Relevant Terms: Opposition to the US Supreme Courts Decision to Overturn Roe v. Wade
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
This study investigated people's attitudes on social media towards abortion right after the leaked version, and the official version were put out, as well as the factors that influence and reveal human attention to independent rights and gender equality. Quantitative and qualitative data were collected through Twitter. Data from quantitative analysis and topic modeling suggested people's different focus topics on abortion, the most relevant focused hashtags in this event, and how people's main concern towards the issue shifted after the official version came out. Results from the qualitative analysis indicated how human's reactions towards abortion have been displayed through platforms and what parts are the most concerned in this incident. This research emphasizes the critical role of human attitudes in controversial decisions and how media separate them into different groups and tags to allow a more extensive community for people to discuss and protest.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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