Bibliographic record
Abstract
In April 2020, Amazon released a new comedy series called “Upload.” The show extrapolates a future in which human consciousness is successfully simulated in silico. In this world, individuals pay to be “uploaded” into a digital afterlife. When uploaded, human consciousness is converted into data and executable code, which can be edited, reset, or even deleted depending on each upload’s membership and payment plan. The show breaks the boundaries between reality and virtual reality, consciousness and artificial intelligence, and even life and afterlife, entangling existing legal questions in novel ways. By addressing three of these legal issues, we hope to highlight how science fiction may help launch a more nuanced conversation about what is artificial in artificial intelligence, what is virtual in virtual reality, and what is digital in digital rights. We argue that becoming early adopters of a new reconceptualized language around “us” and “them”, the “self” and the “other,” can perhaps future proof our society from the technological perils that await us.
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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".