Text classification using labels derived from structured knowledge representations
Bibliographic record
Abstract
Derek Ruths.After graduating from McGill in 2010, several opportunities presented themselves, including working for a big company and studying in Europe's most prestigious universities.However, a chance meeting with Derek made me put all those options aside and choose to pursue a Master's degree at McGill.I immediately sensed that Derek would give me the liberty to choose where I wanted to go intellectually, and then show me how to get there.What followed was two years of trust, where we seized every opportunity to explore different ideas.As with everything we did, the work we present here is a collaborative effort that wouldn't have been possible without his constant input and support.As I said, I am deeply grateful for his supervision and friendship, without which I wouldn't be where am I today.I want to thank the Fonds de recherche du Québec -Nature et technologies for their financial support, and Google for the freedom to pursue my Master's degree and the opportunity to develop interesting products that shaped my understanding of this industry.Je veux remercier ma famille et proches amis pour leur support quotidien.Merci pour tout! iii
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".