Key issues around copyright and social media: ownership, infringement and liability
Bibliographic record
Abstract
... The terms and conditions of social media platforms dictate the ownership of the material posted by users. However, as argued below, these terms are over-reaching and unfair to the user. This article argues that is particularly impudent in light of social media platforms encouraging users to share both their own original content and third-party content. The more time users spend on their platform sharing content, the higher revenue they are able to receive through advertising. For example, Facebook earned $16.6 billion in advertising revenue for the second quarter of 2019, a 28-per cent increase year-over-year,6 alongside an increase in registered users7 and time spent on the platform by those users.8 As such, the platforms benefit greatly from encouraging users to share content and this is important to bear in mind during the discussion on how much responsibility the platforms should be burdened with in relation to informing and protecting their users.
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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 | 0.033 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.071 |
| Scholarly communication | 0.034 | 0.037 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.033 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".