Bibliographic record
Abstract
Ontario Premier Doug Ford and US President Donald Trump have something in common: both recently issued directives to colleges and universities intended to promote free speech on campus. Premier Ford’s came first. In August 2018, shortly after winning the provincial election, Ford required all colleges and universities in the province to devise policies upholding free speech on their campuses in line with a minimum standard prescribed by his government. The policies were to be in place no later than January 1, 2019. Failure to comply would result in a reduction of operating grant funding from the province. President Trump’s executive order concerning “free inquiry” on American campuses was issued in March 2019. The order states that it is the policy of the federal government to encourage institutions of higher learning “to foster environments that promote open, intellectually engaging, and diverse debate, including through compliance with the First Amendment for public institutions and compliance with stated institutional policies regarding freedom of speech for private institutions.”1 Colleges and universities that fail to do so are threatened with the loss of federal research and education grants. * Associate Professor, Department of Politics, Faculty of Liberal Arts and Professional Studies, York University where he teaches political theory.1 Andy Thomason, “Here’s What Trump’s Executive Order on Free Speech Says”, The Chronicle of Higher Education (21 March 2019), online: . An executive order is a directive issued by the President of the United States in his capacity as head of the executive branch and has the force of law. Trump’s executive order on campus free speech is reproduced in its entirety online.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.054 | 0.017 |
| Scholarly communication | 0.021 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".