Globalisation and its Discontents: A Concern About Growth and Globalization
Bibliographic record
Abstract
It began to worry me, you see, this destruction of fish, this attrition of love that we were blindly bringing about, & I imagined a world of the future as a barren sameness in which everyone had gorged so much fish that none remained, & where Science knew absolutely every species & phylum and genus, but no-one knew love because it disappeared along with the fish. (Flanagan, R., Gould’s Book of Fish: A Novel in Twelve Fish, Picador, 2002). Over the last two decades, the internationalization of accounting has been gathering steam. Fuelled on by academic and popular interest in globalization, advocates of the internationalization of accounting have argued for the promulgation of accounting standards that erase the local in the interest of harmonizing the global (Volcker, 2000; Cooke, 2001). These apostles of internationalisation have not only encouraged the
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".