Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
I'm thankful that after last week's Federal Budget I don't have to talk to you in the grip of the gripes, because some of those stories about an excise on wins 'were enough to turn anyone to drink.But , ^fortunately, they were only rumours, ^although the intent of them was I think to create the spectre of indirect tax rises and then let the community collectively be thankful that the extra taxes did not materialise..2 I'm sure the wine industry heaved quite a sigh of relief on the Wednesday morning, even if a disturbingly large number of other Community groups were rightly upset.To be fair, the Budget made some improvements to the wine industry, especially in the provisions allowing private companies to retain a higher proportion of their profits for re-investment.As well, the partial adoption of the Mathews Report on stock valuation will give the industry some tax relief, although not immediately.The effects of the 50 percent adoption will flow through in the last quarter
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.022 |
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 it