Bibliographic record
Abstract
n Else Lasker-Schüler’s poetry and prose, we nd the desire or wish to be devoured by the love object while consuming the object in turn. In this analysis the merger or turn of phrase is tied to the subject’s own constitutive incorporation of a dead loved one. Now living objects must be loved to death or undeath. It was her mother’s death that guided Lasker-Schüler to live and love on as haunted subject to repeat and rehearse the love object’s loss or departure via fantasies of incorporation. Leigh Gold is currently working as a translator. She trained in German philology at Williams College and New York University. Her contribution refers to her re- cently defended NYU dissertation titled: “Ich sterbe am Leben”: Else Lasker-Schüler and the Work of Mourn- ing. Rodrigo Hernandez trained at the Escuela Nacional de Pintura, Escultura y Grabado La Esmeralda, Mexico City before entering the Academy of Fine Arts Karl- sruhe, class of Silvia Bächli in 2010. His work has been shown in solo and group exhibitions in Alberta, Berlin, Hamburg, Mexico City, Stockholm, and Zürich, among other cities.
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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 | 0.004 | 0.018 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".