Sound Works: Prototyping a Digital Audio Repository for Sound Poetics in Mexico
Bibliographic record
Abstract
This dissertation reflects on the prototyping process carried out for creating and developing the PoeticaSonora’s digital audio repository, focused on storing, editorializing and disseminating works of sound art and poetry readings produced or recorded in Mexico since 1960. While describing the theoretical, technical, and methodological implications at stake in the design, deployment, and refactoring of the PoeticaSonora prototype (PSP), this dissertation speculates on how experimentation and a hands-on approach to sound recordings are essential for advancing fieldwork-based research in the humanities, particularly literary criticism. The notions of voice, inscription, and instrumentality, discussed in depth throughout this work, are essential for constructing a sound-oriented approach to poetry and sound art with the aid of digital tools such as the ones offered by the PSP. After a brief panorama reviewing the many artistic scenes and genres that are present in the PSP, the Introduction frames the project’s importance for both gathering and discerning artistic tendencies in Mexico that have not been properly analyzed by text-oriented approaches to literary criticism. Chapter 1 proposes a decolonial approach on how to establish a duly horizontal dialogue around digital audio repositories in Canada and Mexico. It also delineates the necessary conditions met by PoeticaSonora to design a workflow respecting the features of artistic communities, cultural institutions, and private collectors who contribute to the PSP. After a close analysis to the prototype’s data schema and its design, deployment, and refactoring phases, Chapter 2 discusses how the restraints of database management systems both affected and modified the theoretical and methodological approach followed by the PoeticaSonora team. Chapter 3 focuses on a case study of how women vocal artists in Mexico City use and share sample-looping techniques among each other, as an example of how fieldwork contributed to fix problems in the data schema discussed in Chapter 2, such as the distinction between individual artists and collectives, between singing and reciting voice, and in the use of instruments, apart from their own voices. The epilogue discusses the necessary steps to develop the PoeticaSonora Beta version, as well as to host it in a definitive server with all the institutional, administrative, and political implications this will have on the project as a whole.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".