Bibliographic record
Abstract
When John Fitzgerald Kennedy began his long-shot quest for the presidency, he and his advisors feared his wife was a political liability and would alienate American voters with her aristocratic bearing and tastes. Not only did the Sorbonne-educated Jacqueline Kennedy cultivate a sophisticated style and dress in the slim sheaths and tight slacks favored by Parisian couturiers, she spoke in a cultured, whispery voice and was fluent in several languages. She exuded glamor. To an America used to its First Ladies looking and dressing like Bess Truman and Mamie Eisenhower, Mrs. Kennedy was an anomaly. In this analysis, I trace Mrs. Kennedy's evolution from a political liability to a political secret weapon. By focusing on three foreign trips, to Ottawa, Paris, and to several cities in India and Pakistan, I document the changing lens through which she was viewed by the press abroad, by the press at home, by her husband's political advisers, and - most importantly to her - by her husband himself. By being true to her inner muse, Mrs. Kennedy became an international star and a major political asset to the New Frontier.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".