Bibliographic record
Abstract
Globalization is an often misused concept, although David Leadbeater’s edition employs one version appropriately. Partly the error stems from thinking that globalization is a recent development, despite globe-wide interchanges since at least the late 18th century, even if some continents’ interiors remained relatively untouched until the 20th century. And since the broader spatial reach of corporations has done little to overcome social inequalities and provincial mentalities, the much-touted benefits of globalization are questionable. Indeed, the global practices of most hegemonic states and corporations have tended merely to continue the excesses of labour exploitation and military incursions of the colonial era, exemplified by the use of child labour (Nike among many) and the American/British/Russian unilateralism in diplomatic and military adventures. Even in fields such as health or scientific knowledge, the present supposedly global world has witnessed mostly increasing international disparities. That does not, of course, prevent countries that have large global enterprises from having huge pockets of poor health services and low education, as Canada’s Native population and the average American’s understanding of the world illustrate. Many elements of the latter type of globalization are well presented in Leadbeater’s edited case study of Sudbury. One review essay / note critique
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".