Bibliographic record
Abstract
"Each life story is unique, yet each also entwines with other stories, sharing recurring themes linked to issues of gender, Jewishness, women's education, politics, and migration. The book's first section discusses relatively known analysts such as Sabina Spielrein, Lou Andreas-Salomé, and Beata Rank, remembered largely as someone's wife, lover, or muse; and the second part sheds light on women such as Margarethe Hilferding, Tatiana Rosenthal, and Erzsébet Farkas, who took strong political stances. In the third section, the biographies of lesser-known analysts like Ludwika Karpińska-Woyczyńska, Nic Waal, Vilma Kovács, and Barbara Low are discussed in the context of their importance for the early Freudian movement; and in the final section, the lives of Eugenia Sokolnicka, Sophie Morgenstern, Alberta Szalita, and Olga Wermer are examined in relation to migration and exile, trauma, loss and memory. With a clear focus upon the continued importance of these women for psychoanalytic theory and practice, as well as discussion that engages with pertinent issues such as gendered discrimination, inhumane immigration laws, and antisemitism, this book is an important reading for students, scholars, and practitioners of psychoanalysis, as well as those involved in gender and women's studies, and Jewish and Holocaust studies"--
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".