Global outreach operation to save the world through utopianism financed with negative interest loans.
Bibliographic record
Abstract
Howdy Seb here from Canada. I am running a global outreach operation to save the world through utopianism financed with negative interest loans. I’m also after the Nobel Peace Prize. I do research, development, innovation, implementation, integration, indoctrination and ideation on the human condition while I test advanced medical biotechnology I've developed with the Holy Ghost during my ongoing custom studies in omniology, omniosophy and omnimatics focused on human and artificial intelligence. Pharmaceutical grade nutrition for physical health and mental clarity have allowed me to go beyond a standard and this is the multi-domain solution that I came up with. Can You Help With World-Saving Socio-Economic Development? Utopianism financed with negative interest loans to remunerate custom jobs, subsidize wage inflation, refinance debt, boost human resources, adjust capitalism for the future of work, adapt capitalism for the future of work, support gene therapy efforts through the production of regional variant mRNA vaccine production facilities, establish ideological goals for humanity worthy of the 21st century and beyond as well as finally providing a platform to fix the root causes of most of humanity's problems. More information: https://networks.h-net.org/utopianism-financed-negative-interest-loans https://networks.h-net.org/node/14473/discussions/7815113/21st-century-utopianism-possibility-must-be-explored-and https://othernetworks.org/Utopianism_Financed_With_Negative_Interest_Loans https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3839738 https://ubiverse.org/posts/utopianism-financed-with-negative-interest-loans-can-you-help-with-world-saving-socio-economic-development Thank you for your time, good luck, God bless and godspeed. Seb. savetheworldandhumans@gmail.com Transit: 02391 A#: 504-196-7 BID: 003 Thank you so much!
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.427 | 0.161 |
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".