Bibliographic record
Abstract
This paper is focusing on the concept of “freedom of expression”, a philosophical notion that could be framed either as a norm or as a value. After clarifying the contexts of its utilization, I am interested in analyzing the ways in which an ethical framework for freedom of expression is proposed. I do this by investigating a prominent example from the contemporary political life of Canada, namely Justin Trudeau’s response to Emmanuel Macron’s position concerning the publication of Muhammad’s caricatures. I will argue that differences in conceiving freedom of expression go hand in hand with an antinomy between a consequentialist ethic and a deontological ethic. As such, disputes between norm and value and between consequentialism and deontology constitute sub-debates of the central debate on the limitation or ethical regulation of freedom of expression. This investigation reveals, yet again, the presence of the motif of dissymmetry at the core of public debates: more often than not, discussions are not dominated by the intrinsic logic of the arguments, but by discursive force.
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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.114 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".