Bibliographic record
Abstract
The 2007 annual report of Macquarie Bank runs to 120 compendious pages, its uncompromising inkiness broken only by a few small photos, pie charts and graphs. The text is unsigned, and there is not a single image of anyone at the bank, even its managing director, Allan Moss. Curious: Macquarie Bank used to be known for attractive, even imaginative annual reports. There was one that had peepholes in the pages; another had arty metaphorical images that paralleled aspects of investment banking with other occupations. But Australia's most admired corporation does nothing without a reason. It's almost as though the more successful it becomes, the more it aspires to impersonal, monolithic status. Then there's that name. Apple Computer recently relaunched itself as Apple, explaining that it had left behind old-fangled 'computers'. The 'bank' in Macquarie Bank seems almost as archaic. In the financial world, the organisation is neither fish nor fowl, exhibiting characteristics of investment bank, asset manager, buy-out house, private-equity arranger and hedge fund. Perhaps that is why the custom has become simply to enumerate the assets under its control - a clich beloved of the news media that nonetheless conveys something about Macquarie's diversity and geographic spread. Airports, antennae, casinos, car parks, turnpikes, tunnels: whether in Canada, Korea, Britain, Belgium, Singapore, Spain, Taiwan or Tanzania, all come alike to Macquarie's sophisticated metrics and financial heft.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".