Bibliographic record
Abstract
In June of 2013, my youngest daughter, Grace, and I crossed the border into Canada from the United States, our applications for work and living permits in hand. We were directed by border agents to the immigration centre, and when our turn came to speak with an agent, as she completed our paperwork, she asked Grace what she most looked forward to about becoming Canadian. Grace said confidently, “Tim Horton’s!” This produced laughter among every agent close enough to hear. As our agent returned our documentation, she told us we would find a Tim Horton’s at the first exit after the border and welcomed us to Canada. The relief I felt was palpable. I had already promised my family that I would never uproot them to change jobs again. But more than this, I felt the anguish of living in a nation now adrift on rising tides of white supremacy and racism, now sinking in a sea of right-wing extremism and protofascism receding. Some time would pass before I allowed myself to see, to hear, to know that not all border crossings were as easy, as welcoming as ours.
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 | 0.099 | 0.255 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.068 | 0.010 |
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".