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
The U.S. presidential elections have been the site of racism, sexism, classism, ableism, and ageism among other problematic issues. While the 2024 U.S. presidential election continues to be fraught with numerous “isms” and accusations, the focus in this essay is on a new and powerful discrediting tactic: the whisper of cognitive decline. Accusations of cognitive decline not only cast doubt on a politician’s ability to think and act clearly—an unpardonable sin in leadership—but also builds on ageist stereotypes that make such accusations seem credible despite evidence. Ultimately, I argue that because Donald Trump and Joe Biden are wealthy, white, educated men of roughly similar ages, seventy-seven and eighty-one respectively, targeting their cognitive status feeds into social stigmas and fears that are difficult to counter and that, unfortunately, the harm caused by this new level of attack negatively affects older people and people living with neurocognitive disorders.
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 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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