Bibliographic record
Abstract
Many Australasian-Anglo-American jurisdictions including Queensland, other Australian states, the Australian Commonwealth, central government in Britain, the U.S., Canada and New Zealand (Department of Finance and Administration, 2000; NZ Treasury/State Services Commission, 2002; Queensland Treasury, 1997; Treasury Board of Canada, 2000), are presently debating over “managing for outcomes.” Throughout this chapter, the acronym MFO is used to stand for this whole movement even though it implies greater coherence than exists. There is a definite movement in this direction in Australasian public services with the emergence of widespread rethinking about its purposes and characteristics. It is driven in some jurisdictions by ministers wanting to know about actual policy outcomes and less about the shiny-chrome management systems behind them and, in other jurisdictions, by senior managers in central agencies and some line agencies who are rediscovering the real purposes constituting public management. There is also some back-pedaling in relation to some aspects of the economic reform agenda that was applied too hard during the late 1980s and 1990s in this part of the world. There are also some that claim that MFO is a logical extension of the first stage of reform undertaken during the 1980s and 1990s – one in which outputs rather than outcomes was the primary focus.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".