Bibliographic record
Abstract
Millions of people have been the victims of Internet scams in recent years. The Internet Financing Illusion explores one businessmans personal journey through the perplexing and often dark world of cyberspace and profiles the faceless financial scam artists who lurk behind keyboards and computer screens. In 1999, Vincent Panettiere started searching for ways to finance his film production company. More than two hundred leads and several months later, he was unwittingly thrust into an Internet adventure that would consume his life. Anamika Biswas of Kolkata, India, said she had $33 billion to invest in several companies located in the United States, Bermuda, Canada, and Australia. For most of 2003, this twenty-four-year-old woman led Panettiere and approximately twenty other individuals and businesses with the precision of a military commander leading the enemy into a blind canyon. In this powerful and cautionary tale, Panettiere includes the actual e-mails he exchanged with Biswas in order to help others recognize the inconsistencies, lies, and manipulative text that Internet scam artists use to lure their victims. The Internet Financing Illusion delivers firsthand information and invaluable guidance to keep you scam-proof in cyberspace.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".