INVESTIGACIÓN SISTEMÁTICA DEL TEXTO DE UN ADOLESCENTE DE LA NOVELA “DESPERTAR DE PRIMAVERA” DE FRANK WEDEKIND
Bibliographic record
Abstract
En el texto se estudian frases de una adolescente de la obra “Despertar de pri-mavera” de Frank Wedekind, mediante el Algoritmo David Liberman (ADL). Este Algo-ritmo implica un metodo de investigacion sistematica que considera las fijaciones pulsionales y sus destinos, asi como el estado y funcion de estas. Con este recurso se pone en evidencia la articulacion en una de las protagonistas de un deseo suicida, que se expresa en un dejarse morir en manos de la progenitora y de una monja, en las cuales cobra predominio un intenso deseo de filicidio. Palabras clave Adolescencia Defensa Felicidio Suicidio ABSTRACT SYSTEMATIC INVESTIGATION ABOUT THE TEXT OF AN ADOLESCENT FROM THE NOVEL “SPRING AWAKENING” OF FRANK WEDEKIND In the following text, the sentences said by a teenager girl extracted from the book “Spring Awakening” of Frank Wedekind, Will be studied using the “David Liberman’s Al-gorithm” (DLA). This agorithm employs a systematic investigation method that considers the drive fixation and its destinies, as well as the state and function of these. Applying this technique, it is possible to find suicidal desires in one of the main characters. Those desires expresses theirself by allowing the character to “let herself die” in hands of her progenitor and a nun, where a strong desire of filicide becomes predominant. Key words Adolescense Defenses Felicide Suicide
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".