Bibliographic record
Abstract
It is a challenge to simply state the law about social media, because there is no such thing as “social media law.” Rather, the law bumps up against social media in many ways. In some cases, existing law can be seamlessly applied to new technologies and means of interaction. In other cases, entirely new paradigms will likely need to be adopted to handle new challenges. Many other cases will fall somewhere in between.\nOur goal in this Law Simply Stated is to provide some background on the nature of social media themselves, and then to state the basic law in a number of applicable fields. In particular we shall begin with discussion of the definition of “social media,” along with consideration of what we see as an important analytical theme: is this topic a topic? Do the challenges posed by social media have a single solution? Are they matters to which the law can simply adapt, or must new approaches be created? Can the same answer be provided in each context?\nFollowing that we will move to discuss basic principles of law in a variety of areas which are relevant to social media. These are: judicial notice; courtroom management; civil discovery; privacy; admissibility of electronic documents; criminal evidence-gathering; employment law; defamation; and finally, cyber-bullying.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.017 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".