Bibliographic record
Abstract
Most people are coming to Newfoundland this summer to celebrate John Cabot's historic landing in 1497, I am here to celebrate the folks who greeted his arrival and helped him ashore -or maybe they hid.I Iwas pondering how to handle this notion of "discovery" -which First Nations people predictably find both insulting and amusing, last week as the annual multicultural Caravan celebrations were just gearing up in Toronto.The First Nations pavilion there was also recognizing 1497 as an important meeting point but, to quote their spokesperson, they "see 'discovery' more as people looking at native people through new eyes.2 " Iwould like to affirm that it is the related task of hearing anew which is my objective here today.In the next hour, I want to reflect with you on how singing, the very sound of Aboriginal and European voices has been and continues to be a site where we -their descendants -negotiate who we are and how we relate.I start with a very basic question: Who do you think has a singing voice?
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 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".