Bibliographic record
Abstract
“Wherever I Am, Nevertheless You Are” is a sonic autocartography built from a zine I created in the spring of 2024 entitled “My Dinner with Diaspora.” The zine follows myself, a 1.5-generation Pilipinx-Canadian migrant, haunted into having dinner with my five-year-old self right before immigrating from Manila to Toronto. My web of diasporic identity grew after I moved from Toronto to Vancouver for graduate school. This sound work explores my multisensory experiences in Manila, Toronto, and Vancouver through bodies of water and social networks. While I satisfy longings for Manila’s saltwater seas through Vancouver’s Pacific shores, I developed my sense of Pilipinx identity within Toronto’s lakes and diasporic communities. In this piece, I express the ways these waterscapes compound to form my feelings of everywhere/nowhereness as a Pilipinx migrant. The work consists of a score I have arranged, including the sounds of synthetic and acoustic instruments, conversation, my childhood voice, the movement of water, and meals I have shared. In the accompanying text, I reflect on what is generated from the convergence of imaginaries where my senses in one location conjure memories of another, and memories cement with the discovery of diaspora anew.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.009 |
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".