Bibliographic record
Abstract
On May 28, 2011, the Economist published reports about Australia. Inside a cover dominated by a golden map of Australia with the caption “The Next Golden State,” referring to the mining boom, is an article about the diversity of its population, titled “The Evolving Platypus.” A most evocative metaphor.’ This rare monotreme is an egg-laying mammal with a duck bill, a beaver tail, and flipper limbs, as unique as the continent where it lives and the nation that now inhabits it. To outsiders, it is remarkable that a meager 22 million people of disparate cultures and races managed to occupy a vast continent, thrived, and prospered. The nation has certainly evolved in the last 60 years: in the 1940s Australia was 98 percent Anglo-Celtic; today 26 percent of the population was born overseas (compared with 21 percent in Canada, 14 percent in the United States, and 10 percent in the United Kingdom). 2 The 2001 and 2006 Census showed the second most spoken language is Chinese (Cantonese and Mandarin) and the third is Arabic. Reconciliation, a grand narrative and a national imperative, can no longer be seen as unfinished business only between blacks and whites, the main historical antagonists. 3 Reconciliation in the twenty-first century must involve other migrants: yellows, browns, mixed races, and blacks from Africa. This topic is rarely explored. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".