Bibliographic record
Abstract
D olores Claiborne has aged well. Now in my mid-fifties, I see this character as something of a role model. In some ways, I am Dolores. Mind you, I did not murder my ex-husband, but I have to admit the thought did cross my mind. I admire the way Stephen King fashioned a female character unfettered by others’ opinion of her. He crafted a woman with a brusque surface, but a deep underlying sense of love and purpose. She is a character who acted with intensity and evolved over time. Her story pivots around the year 1963. Stephen King chose the year well. That was the year a full solar eclipse crossed Alaska, central and eastern Canada, and Maine. The event drew a great deal of media attention and a beautiful article about the eclipse appeared months later in the pages of the November 1963 issue of National Geographic (Espanek). One can imagine a young Stephen King perusing the article. Coincidently, that was also the year feminism reemerged from its years of remission after women’s suffrage in 1920. It was the year feisty young women who grew up in “a man’s world” would be presented with the option to “become the men we wanted to marry” (Steinem 263). Betty Friedan’s The Feminine Mystique was released in 1963. It was also the year of the Equal Pay Act and the publishing of the Report on the President’s Commission on the Status of Women , chaired by Eleanor Roosevelt until her death in 1962. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".