Bibliographic record
Abstract
The common law engages with social media in a manner that overlaps with defamation: seeking to balance the competing interests of free speech and protection of reputation. Employment law prompts a question as to how the law on this topic is developing. Adjudication of the concept of business reputation is the comparative focal point. Canadian employment law is developing a balance between protecting business reputation and workers’ free speech (though there are issues arising therein). In the UK, however, the term has been more of a blunt tool. As a result, a juxtaposition has arisen: speech in traditional media is better protected than that of workers using virtual social platforms. Using the contrasting case law as an example, the argument pursued here is that law must develop in a manner that establishes scope for remarks by workers on user-generated content platforms while protecting business reputation. To permit discipline for any form of social media remark would be inconsistent with the spirit of twenty-first century defamation law that has expanded protection for 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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.075 |
| Scholarly communication | 0.025 | 0.020 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.016 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".