Tapping the Potential of Senate-Driven Reform: Proposals to Limit the Powers of the Senate
Bibliographic record
Abstract
In the immediate aftermath of the 2014 Senate Reform Reference, there was considerable talk about the limitations that the Supreme Court had put on Senate reform. Some political leaders expressed frustration and declared that we are left with the status quo. But, that view both misunderstands what the Court said and underestimates what can be achieved through non-constitutional means. There is much that can be done simply with the political will to change the Senate situation without resorting to constitutional amendment; senators already have the power to effect some serious reform from within. This paper focuses on an unorthodox suggestion: that substantive reforms might be achieved through changes to the Rules of the Senate governing its legislative process. With some changes to both the legislative and appointment processes, substantial improvements to the Senate are both possible and achievable. The result would be a Senate better able to perform its intended function as a chamber of sober second thought. It would also answer the most serious concerns about an appointed Senate’s role in a modern democratic system.
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 | 0.048 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 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".