MEMORIAS | electronic literature + live coding performance
Bibliographic record
Abstract
Memorias is a web-based artistic project by Jessica Rodríguez developed through the Estuary platform —an online platform to host live coding languages. It is based in six autobiographical writings connected to the way she “hears”, “writes”, “watches”, “reads”, “sees” and “listens” to the word. Through these texts, six code works were designed and programmed, hybridizing natural and computing languages by parsing three existing live coding languages: Tidal Cycles, Punctual, and CineCer0. Together, Memorias’ languages collide different materialities as well as visual and sonic approaches, going from voices in English, Spanish, Cello and Paetzold samples, audio and visual synthesis, and pre-recorded video clips. This project explores how speech and literature — in its written form— can be used as interfaces that allow the performer to communicate both, with the computer and the audience. Additionally, speech —in its sonic form— is moved through space and time, expanding the possibilities of spoken literature by producing unlimited variations of the “original” autobiographical writings. Within the space/time of the piece, the audience can experience different ways, textures and logics to approach visual and sound through the use of language as a memory trigger. For this conference, Memorias is be presented as an online collaborative performance by andamio.in, including Jessica Rodríguez, Rolando Rodríguez, Alejandro Brianza, and Luis M. Zirate. Credits: Voice in English_ Vic Wojciechowska (Canada) // Voice in Spanish and text edition_ Rolando Rodríguez (Mexico) // Cello_ Iracema de Andrade (Brasil-Mexico) // Paetzold_ Alejandro Brianza (Argentina) // Technical advisors_ David Ogborn (Canada) & Luis N. Del Angel (Mexico-Canada)
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.125 | 0.023 |
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".