Bibliographic record
Abstract
Pre-election debates are one of the most important steps in the electoral process – indeed, they serve an important public interest as they inform the American public about the issues of the day and offer a forum by which candidate proposed solutions may be heard. However, pre-election debates are led by moderators who generally do not have expertise in many of their key topic areas, such as law or judicial studies; and because of this, the propositions and arguments made by candidates in the pre-election time may be de-contextualized during debates such that the voting public may be misled in terms of the practicality of candidate positions. It is not unusual for individuals to unwittingly make propositions which insufficiently account for the confines of governmental structures, norms, and institutions in important ways. Likewise, it is not expectable for candidate to have absolute expertise in all areas of the debate, such as from health care to international law. This presents a real and pressing problem or issue for the quality of debates and democracy. It would be useful for pre-election debates to have additional facilitators present to provide basic factual and scientific information, as well to define key terms and principles relevant to American government and political life. Thus, given the current format of pre-election debates, this policy brief offers proposals to increase voter awareness and thus strengthen American democracy through amendments to the pre-election debate format for general elections.
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 | 0.055 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".