Discussion following the Remarks of Mr. Gherson and Mr. Strang Proceedings of the Canada-United States Law Institute Conference on Multiple Actors in Canada-U.S. Relations: The Role of the Media and Public Perceptions
Bibliographic record
Abstract
MR. CRANE: I think we've had two terrific presentations.It sort of worries me a bit that we have so much coverage of Canada, and yet we have the resulting Canadian views on what it's like to live in the United States, which makes me wonder about the stories we're actually running.But I think that having heard two very interesting presentations.Why don't we have some discussion?And the first hand I saw belonged, I think, to Henry King.PROFESSOR KING: I had a question for Mr. Strang.It seems that I listened to your presentation, and it seems that there is a Herd psychology in the newspaper field, we don't do it because others don't do it.And, also, there are many people who feel, for instance, Canada doesn't get much coverage in the Plain Dealer that, say, Cleveland could be the gateway to Canada, and it's right across the lake.Can you dare to be different in this world or do you all have to do the same thing?And, in other words, isn't the function of newspapers to investigate and educate?Are you doing your job?That's what I'm saying.And I don't know the answer, but maybe you could enlighten us on this.MR.CRANE: Jim would love to answer that question, Henry, and he's glad you asked it.MR.STRANG: I can scarce wait.Well, yes, there is a Herd -MR.CRANE: Thank you, Jim
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.007 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.035 | 0.049 |
| Insufficient payload (model declined to judge) | 0.025 | 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".