JD student Julia Kalinina and Aoife McCormick, an exchange student last year from Trinity College Dublin, each win a prize for outstanding paper
Bibliographic record
Abstract
JD student Julia Kalinina (pictured) has won the 2019 Advocates In Defence of Expression in Media/Canadian Media Lawyers Association writing prize for her paper, “Political Advertising in the Age of Social Media.” She wrote the paper in Professor Craig Scott‘s International and Transnational Law Intensive Program (ITLIP) last term. Ad IDEM/CMLA offers annually the Ad IDEM Writing Prize for a paper promoting freedom of expression. In addition, Kalinina has received an invitation to the Association’s annual conference taking place in Toronto November 8 to 9.\nIn other news, Trinity College Dublin student Aoife McCormick, who was in Adjunct Professor Tony Duarte‘s entertainment and sports law class last year while on exchange at Osgoode, has won the A&L Goodbody Solicitors prize for most outstanding article published in the current volume of the University College Dublin Law Review [Vol 19 2019]. The title of McCormick’s article is “The Immortal Icon: A Critical Analysis of Post-Mortem Publicity Rights in the United States.” In an email message to Duarte informing him of her award-winning paper, McCormick thanked him “for teaching such an engaging class and for your help on my essay.”
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.132 | 0.044 |
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".