Bibliographic record
Abstract
BPS: The reason we wanted to do this interview is from the process point of view, the past session seemed to be the most remarkable one in about a decade.It was an extraordinary example of the Opposition's ability to put a spanner in the works.The Opposition extended this session, forced the government to make some compromise in terms of scheduling when things would be.Can you give our readers just a background on your rise to House Leader.What the job is about?AS: I was appointed House Leader after the summer of 2013, when there was a cabinet shuffle.The Premier asked if I would take on the role.I was not that surprised as traditionally House Leader has gone along with the role of the Attorney General.I guess they presume that the House Leader who has to be reasoned and negotiate, often those would be qualities you would hope to have in the lawyer who fills the role of the Attorney General.So I wasn't surprised.I had served as the unofficial or backup house leader for Jennifer Howard, who was both house leader and Finance Minister in the last session.So I would spell her off and I would
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.040 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".