Bibliographic record
Abstract
Sergio Blanco’s play Tebas Land (2012) is an exploration of his concept of autoficción [self- or autofiction] in which Blanco shows how creative acts like writing and performance reflect the ways that truth and fiction coexist in our understanding of self and other, and in which identities and staged realities are relational rather than binary. The stage functions in Blanco’s work not as a place to arrive at the real reason or motivation for an event or its consequences, or amplify the context for unexplored histories to enter into the dialogue, but rather as a place where truth is always already fiction. Blanco sets up vectors where fact and fiction are constantly made, undone, and remade . Tebas Land features Oedipus’ infamous patricide as its point of departure, and Blanco uses this story to explore familial murder and the ethics of reconstructing it on stage, as well as the feasibility of re-presenting any “real” or performed act as authentic. Blanco toys with frameworks like theatre of the real, nested space and time, the act of watching and being watched, and objects that shift meaning and thereby defy documentary impulses to fix significance to context.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".