Techno-authoritarianism & copyright issues of user-generated content on social- media
Bibliographic record
Abstract
Lawrence Lessig in “Code: Version 2.0” presents “code” as the new law and regulator of cyberspace. Previously, techno-authoritarianism represented state sponsored authoritarian use of the internet, and digital technologies. It has now experienced a takeover by private entities such as social media platforms, who exercise extensive control over the platforms and how users interact with them. Code, akin to the law of cyberspace emboldens social media platforms to administer it according to their agenda, the terms of use of such platforms being one such example. The terms of use, which are also clickwrap agreements, are imposed unilaterally on users without scope of negotiation, essentially amounting to unconscionable contracts of adhesion. This paper will focus on one specific angle of the impact brought upon by the terms of use, user-generated content on social media platforms, and their copyright related rights. This paper will doctrinally assess the impact the “terms of use” of social media platforms has on user-generated content from a copyright law perspective, and consider whether the terms amount to unconscionable contracts of adhesion. This paper revisits, or reimagines this problem surrounding copyrightability of user-generated content and social media platform terms of use from the lens of techno-authoritarianism and the influence of code.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".