Freedom of Speech and Academic Freedom in Higher Education in England
Bibliographic record
Abstract
ABSTRACT: This article considers the context, development, and significance of the Higher Education (Freedom of Speech) Act 2023 . The Act was relatively unusual in aiming to increase the normative strength of freedom of speech. The central justification given for the Act was the need to respond to an increasing number of interferences with free speech and academic freedom occurring at universities. The growth of a "cancel culture" was having a "chilling effect" on students, staff, and visiting speakers. The article examines a range of high-profile cases and incidents that have attracted political and media attention. Many of these have concerned contemporary debates related to trans issues and identity politics. The issues discussed in the article are of wider international interest. Similar controversies have been experienced in universities in other states. The article makes comparative reference to developments in the field in the United States, Canada, Australia, and New Zealand. The article examines the perceived issues and evidential bases for the Act, reviews the legal duties, and analyzes the key legal concepts. It considers these in terms of compatibility with the European Convention on Human Rights (1950). It concludes by addressing three thematic issues: (i) a Model Code; (ii) challenging university ideologies; and (iii) securing cultural change.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".