Bibliographic record
Abstract
Transhumanism is a philosophical worldview so driving that in some circles, it might almost be called a religion. Transhumanist belief holds that the rushing integration of humans and machines is inevitable and it moves to deeply alter the nature of human existence. Transhumanists believe that, properly harnessed, such integration can become a force of ultimate good. Reaching dominance in the technology sector, transhumanism's tenets and predictions have begun to drive the actions of multinational companies such as Google. If technology is left to take its current path, what are the plausible outcomes? How does the accelerating pace of technology stand to change the world's economy and the lives of average citizens? What risks (economic and ethical) are there to the fulfillment of transhumanist ideals by corporate powers, and how might they be mitigated? This dissertation makes no claim to a concrete solution, but raises the issues which are beginning to confront modern business and political leaders, and will only grow in the future. It implores such leaders to look further, and to make preparations to employ the advances to come to their best advantage, and to the best advantage of the world.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".