Seizing the Moment: Learning from Humanely Relational and Interdisciplinary Soundscapes
Bibliographic record
Abstract
Lessons learned from George Sánchez have been at the heart of the work I do with undergraduate students at the University of California, Irvine (UCI). Many of them face enormous stress as a result of being (or being related to) undocumented Latina/o immigrants in the United States. During the 2011 Winter quarter, several of those students conferred with me about their intention to invite fellow UCI undergraduate students enrolled in our “Histories of the U.S.-Mexico Borderlands” course to join UCI Dreamers. This is an undergraduate student group that supports undocumented immigrant students at our campus as they struggle to finance, complete, and derive full social and intellectual benefit from their undergraduate education. This initiative situated our Chicana/o history course as a productive common ground, a space for this generation of women and men to act in support of each other. Attending UCI Dreamers meetings was not an automatic or random decision but rather the outcome of a series of interactions, discussions, and experiences. Prominent among these was our consideration of music and musical soundscapes that have influenced how life in the US–Mexico borderlands is lived and discussed.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.018 | 0.031 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".