Oye mi canto: Valuable Journalism as a Tool to Tell the Stories of a Diaspora
Bibliographic record
Abstract
Valuable journalism—a concept that aims to enhance civic engagement and news consumption by making journalism a more enjoyable and meaningful experience—offers a potential response to the contemporary challenges faced by journalism as an institution. This research-creation project explores how this approach can be applied through the production of a podcast that tells stories from the Latin American music scene in Montréal. Rooted in the personal experiences of Hispanic musicians, the podcast navigates themes of immigration, identity, cultural clashes, and adaptation, while highlighting these artists’ contributions to Montréal’s cultural life. This project highlights lifestyle journalism’s political significance and relevance. It responds to the lack of media coverage of Hispanic musicians in the city and examines how music journalism can amplify the voices and traditions of the diaspora in a way that is both informative and entertaining. It also explores how, through the use of engaging storytelling, music journalism can educate a non-Hispanic audience about Latin American music, its instruments, its cultural significance, and the lived experiences of the people who create it. This project demonstrates how entertainment and information can coexist to create journalistic pieces with social and political impact—promoting empathy and appreciation for the Hispanic community in a context where anti-immigration discourse, particularly against Latin American people, is spreading rapidly.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".