Bibliographic record
Abstract
Freedom of speech is the ability to express your thoughts and opinions without facing consequences such as retaliation or censorship. The core questions are: (1) what the balance between the person’s rights is, and the level of interference from the state, and (2) protect the community from organized attacks. This paper will describe and break down some of the complexities surrounding freedom of speech, its limitations and the balance between freedom and hate speech. This paper believes that free speech should not be censored because it is a fundamental right that improves the development of societies. It allows for the open exchange of ideas, creativity and innovation, and will enable people to share their thoughts and opinions without fear of censorship or retaliation. Free speech is essential for holding governments accountable and ensuring different perspectives are heard. Without it, many innovations and significant discoveries may have not been possible. However, one of the most challenging aspects of free speech is determining the limits for negativity and harm. The ability to create harm with words should be unacceptable. This was shown in the case of R v Keegstra in 1990, which dealt with hate speech in Canada. The case involved promoting hatred towards different ethnic groups, especially against Jews. Thus, showing the need for boundaries. But there are also cases where free speech faced consequences, as it was improperly dealt with, such as the case of Aditya Verma, a British-Indian student who made a threatening joke to his friends. This paper will analyze this case to show the complexities of free speech.
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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.075 |
| Scholarly communication | 0.025 | 0.031 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.012 |
| 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".