“Graphics, Social Movements, and the Race to Eradicate Abortion: Why America Will Take Gold”
Bibliographic record
Abstract
Skeletal figures of Holocaust victims, wounds and scars of the enslaved, blackened lungs of the smoker; powerful images convey powerful narratives. Over the past century, media has become increasingly pervasive. For social movements, this tool played a key role in achieving mass societal change. Looking to mimic a lasting paradigm shift, pro-life groups have realized that images are the catalyst for change. Ignoring the normative element of abortion, it is important to acknowledge two common goals shared by the pro-life and pro-choice communities. First, both desire to help women. Second, both want to reduce the number of abortions. The obvious disconnect, is the means under which both goals are met. However, over the past decade, the efforts of various ‘Centres for Bio-Ethical Reform’ have shown that one of the most effective methods in achieving both goals has been through graphic image campaigns. It will be argued that in order to help couples make informed decisions, and reduce the number of abortions, images of human development and abortion must be readily available to couples in crisis pregnancy. Using the findings and testimonies from the Centre for Bio-Ethical Reform Florida 2011 mission, it can be demonstrated that convictions about abortion change in face of graphic imagery. Because it does not overtly challenge current legislation, but instead decreases the number of abortions, it ought to be honoured by both parties. Under the protection of the first amendment, pro-life groups in America can freely share these images to encourage discussion. In Canada, ‘freedom of speech’ and ‘freedom of expression’ are often compromised in the face of adversity; conclusively the pro-life message is often silenced. Thus, by virtue of being able to share the reality of the procedure, Americans are leading in the race to eventually eradicate the perceived necessity of abortion.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".