Bibliographic record
Abstract
Premier Rae had mentioned this morning that disputes between Canada and the United States are not just discussed between the federal governments of the two countries. Disputes are a matter of public diplomacy and every interest group is competing for public opinion on both sides of the border. Therefore, this afternoon we can explore the various groups that are in that competition for pubic opinion and the various groups that are involved in Canada-U.S. disputes. I thought we would start with the definition of what an NGO is. We are not going to come to any conclusion on this matter here today. It is much debated. However, one big distinction is whether you include in businesses, for profit entities, or only non-profit entities. The standard definition or at least the most commonly used definition only refers to non-profit entities, such as environmental groups, human right groups, etc. A somewhat broader definition would include associations of businesses, not the for-profit entities themselves, but at least the associations they enter into. While it is important to be aware of these distinctions, I am going to focus on the most common or narrower definition of NGO, excluding the for-profit entities for the moment.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.039 | 0.020 |
| Scholarly communication | 0.018 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".