Bibliographic record
Abstract
My mother bought me a mug: it says, in big letters: YOU CAN’T SCARE ME. I RIDE A MARE. The mare I ride now is the fourth mare I’ve owned. I have now also spent most of my professional life looking at horse magazines and horse people’s memoirs from the nineteenth and twentieth century, particularly in the U.S., Canada, and the United Kingdom and in the realm of Thoroughbred raising and racing. I wasn’t looking for information about mares, but I began noticing how assumptions about them had shifted over the relatively brief history I was examining. In the nineteenth century, mares ran in open competition regularly, a practice that still persists in flat racing in Europe. But in the United States, around the turn of the century, attitudes about mares began to shift, and sex-segregated competition became the rule after World War II. I began to think about how the history of mares seemed to map with striking directness onto the history of women. Second-wave feminists embraced mares as barrier breakers; one columnist opined after Genuine Risk’s Kentucky Derby victory, “in your face, Phyllis Schlafly.” And in the twenty-first century, fans have once again idolized superstar mares like Zenyatta, Rachel Alexandra, and Beholder, following their journeys to the broodmare band on social media. My keynote (and my next book) focuses on the relationship of mares’ history and women’s history in the United States, and it also seeks to more deeply address what that relationship has meant to women in times of personal, professional, and historical change and struggle. Watch here
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".