Academic Freedom in the Heartland: Rights Consciousness and the United States Supreme Court
Bibliographic record
Abstract
In late 2013, the Kansas Board of Regents proposed a social media policy, a policy which the board eventually unanimously approved. The policy authorized “the chief executive officer of a state university…to suspend, dismiss or terminate from employment any faculty or staff member who makes improper use of social media.” A strong and unified condemnation of the policy followed, led primarily by the faculty of those institutions and their various faculty governance organizations. This conflict between the free speech rights of academics and the governing authority of government and university administrations in the state of Kansas was neither the first nor last such conflict; U.S. courts had already established a doctrine over the free speech rights of public employees. Therefore, this conflict presents an opportunity to observe how the judicial establishment and definition of rights affects subsequent political conflict and discourse. The conflict over the social media policy adopted by the Kansas Board of Regents raises questions of whether the established judicial articulations of free speech in an academic setting shaped the efforts of Kansas faculty in opposition to this policy and the crafting of the policy itself.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.036 |
| Scholarly communication | 0.024 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".