The politics of reading the autobiographical I ââ¬â¢s: The ââ¬Ëtruthââ¬â¢ about Outi
Bibliographic record
Abstract
This article takes the genre of autobiography and a case of one womanA¢â¬â¢s autobiographical texts as its starting point in examining the possibilities of combining theoretical feminist discussions with empirical analysis, or, the personal with the political. The article focuses on the autobiographical fragments of an A¢â¬Aamateur autobiographerA¢â¬Â Outi, an actor and a feminist, and studies the ways Outi reworks her identity over years. The analysis shows that the quest for the A¢â¬EtruthA¢â¬â¢ about oneself is futile, and the A¢â¬Ereal meA¢â¬â¢ is only a cherished illusion. However, the lack of stable identity categories opens up a space for feminist politics. Distinguishing the various levels of autobiographical I is used as a method in order to present a subtle reading of autobiographies that would emphasize the many layers of the autobiographical subject and the constant process of becoming. The relations between the A¢â¬ErealA¢â¬â¢ I , the narrating and the narrated I , as well as the ideological I in OutiA¢â¬â¢s autobiographical writings are identified and analyzed in order to demonstrate how the separation of the I 's can help in combining the discussions about the subject in feminist theory and the concrete empirical analysis of gendered lived experiences. Additionally, distinguishing the I A¢â¬â¢s is a tool for feminist politics, and a tool for ethical reading of the autobiographies of unknown women.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.059 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".