Bibliographic record
Abstract
Charlotte Perkins Gilman originally wrote “The Yellow Wallpaper” (1892) to criticize a medical treatment known as the “rest cure,” which was commonly prescribed for women suffering from “neurasthenia” or a lack of nervous energy. Gilman had personally experienced the “rest cure” as a period of intense suffering, and she was pleased to learn that her story had successfully convinced physicians to alter their treatment methods. As neurologists stopped diagnosing “neurasthenia” in the early twentieth century, however, Gilman’s story gradually fell into obscurity. It was not rediscovered until the early 1970s, when it was recognized as a landmark of feminist literature. It has since been the topic of countless essays that have struggled to explain how the apparent madness of the protagonist can also be interpreted as a form of feminist emancipation. Helen Pinsent’s paper covers much of the same territory, although she does two things that are highly original and innovative: 1) she discusses Gilman’s story not in terms of “neurasthenia” but rather in terms of non-unitary theories of the mind and 2) she shows how this new scientific context potentially resolves the ambiguity of the story’s conclusion by suggesting that “losing one’s mind” and “finding oneself” are not necessarily contradictory possibilities. Pinsent thus implies that Gilman appropriated the scientific concept of “double consciousness” to describe the experience of patriarchal oppression and resistance. —Dr. Anthony Enns
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.051 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".