Bibliographic record
Abstract
The American people have become used to government trickery in foreign affairs—wars and interventions based on lies and falsified evidence, “national security” used to justify the whittling away of privacy, classification of documents to hide embarrassing disclosures, massaging of budget figures to mask outrageous spending on arms, and demands for new weapons when already in possession of an unmatched conventional and nuclear arsenal. Now comes trickery in a different domain: the Trans-Pacific Partnership (TPP), which has substantial bipartisan support and strong presidential endorsement. Eleven countries are awaiting the outcome in Congress as President Obama seeks approval to put the TPP on a "fast track," meaning skipping hearings, public input, and amendments and going directly to an up-or-down vote after 90 days to review. Once passed, the TPP will do for US corporations operating in Asia what the North American Free Trade Agreement (NAFTA) did for them in Canada and Mexico—provide new incentives to send jobs abroad, increase corporate earnings, and downgrade protections of the environment and workers at home as well as abroad.
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 | 0.056 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.026 | 0.017 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.010 | 0.030 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".