Campaign Finances: An Introductory Essay
Bibliographic record
Abstract
Montreal. He may be reached at Filip_Palda@enap.uquebec.ca.In 1903 Lenin along with Julius Martov founded the newspaper Iskra, which was later to become Pravda. At the time, Lenin was a ferocious advocate of a free press and free political discourse. After coming to power we find Lenin addressing a Moscow crowd in 1920 with the words “Why should any man be allowed to buy a printing press and disseminate pernicious opinions calculated to embarrass the government? ” Lenin is not alone in the frenzied attention he paid to controlling speech. In 1971 and 1974 the US Congress passed limits on campaign spending by candidates to the House and Senate and outlawed spending by private citizens wishing to express their private views during elections. In its 1976 Buckley v. Valeo ruling the Supreme Court ruled that campaign spending limits violated the First Amendment’s protection of free speech, but ever since some congressmen and senators, and activist groups such as Common Cause, have sought to amend the constitution to limit campaign spending and replace private funding of elections by state funding. The huge attention campaign finances have won from the media and politicians is due to the fear that money can buy elections and that money can buy political favours. Research
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".