The personal essay as autobiography: A gender and genre approach
Bibliographic record
Abstract
espanolEl ensayo personal como autobiografia es el paisaje generico que recorrere en estas lineas. En concreto, me propongo hacer un analisis comparativo de cuatro libros que pueden leerse como las autobiografias de sus autores, aunque estan escritos como ensayo, incorporando en mi analisis aspectos doblemente genericos (tanto de genero literario como de genero sexual). El “ensayo personal” es un genero autobiografico descrito como “un ponerse a prueba a uno mismo”; como “un testado de nuestras respuestas intelectuales, emocionales y psicologicas ante ciertos temas. Exploro, entonces, volumenes recientemente publicados por la escritora espanola Rosa Montero (La loca de la casa), la canadiense Margaret Atwood (Negotiating with the Dead: A Writer on Writing), el mexicano-americano Richard Rodriguez (Brown: The Last Discovery of America) y el judio George Steiner (Errata: An Examined Life). El incorporar a mi recorrido transnacional las obras de dos hombres y de dos mujeres me permite hacer un fundamentado estudio comparativo de algunos temas relacionados tanto con los generos literarios como con el genero sexual EnglishThe personal essay as autobiography is the generic landscape I will traverse along these lines. Within that field, I do a gender-oriented comparative analysis of four books that can be read as autobiography, although they really are written as personal essays, a genre of life writing described as “a self-trying-out; a testing of one’s own intellectual, emotional, and psychological responses to a given topic.” I explore recent hybrid autobiographical volumes written by Spanish Rosa Montero (La loca de la casa), Canadian Margaret Atwood (Negotiating with the Dead: A Writer on Writing), Mexican-American Richard Rodriguez (Brown: The Last Discovery of America) and European-born, Jewish-American George Steiner (Errata: An Examined Life). Bringing into my trans-national analysis the works of two men and two women allows me to do a reliable comparative reading of a number of genre/ gender oriented issues
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".