AI Comments: Leveraging the Global Knowledge Corpus, One Way or Another
Bibliographic record
Abstract
Submitted to the White House and to the National Telecommunications and Information Administration of the United States Department of Commerce on May 11, 2023 and updated on June 5, 2023 in response to their April 13, 2023 Request for Comments, this paper makes the case for AI as a secondary tool in knowledge management. More central to the question is the need to provide tools for scientists and other knowledge workers to compose and direct the actions of computers and digital networks in their professional and organizational roles and make scientific and administrative processes available for use in integrative and reflective ways. As to AI safety, only when valid and reliable answers are known can AI outcomes be validated. Qualitative and quantitative methodologies, including peer review, are solely capable of being carried out by humans using their wide array of senses and abilities. Once organized and validated in these ways, computers, including AI, can instantly and readily derive contexts and support high-level classification and calculation to apply them at scale.
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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 | 0.036 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.018 |
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".