Bibliographic record
Abstract
It has been over two decades since my article on the peripheralization of the centre was written, and a long time since I had taken a look at it.2 When I was asked by the editors of Alternate Routes to revisit the article, my first thought was that it might simply be a curious exercise in nostalgia. A lot has happened since 1988, but it was interesting to discover that much of the analysis of the article still holds. With continuing global economic restructuring, or globalization as it came to be called in the 1990s, and the neoliberal politics of deregulation, privatization and free trade, the world economy has become ever more integrated under the con-trol of transnational corporations and their respective states. The negative impact on work and welfare has been considerable. Though unanticipated, the original Alternate Routes article provided the basis for my continuing exploration of these trends.3 Peripheralization of the Centre In the Alternate Routes article I had succinctly defined peripherlization as “the transformation of high-wage, liberal-democratic societies into low-wage, authori-tarian societies ” (Broad, 1988: 5). Let me comment on the main thesis of the article, and then I will turn to its relevance for current trends. The thesis of periph-eralization of the centre, as put forward by a number of authors cited in my original article, is drawn from world systems analysis. World systems and dependency theo-
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.027 | 0.030 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.021 | 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".