Power in the global era : grounding globalization
Bibliographic record
Abstract
Preface Notes on the Contributors Introduction T.H.Cohn, S.McBride & J.Wiseman PART ONE Deconstructing Globalization L.Dobuzinskis Transnational Politics: Beyond the National? I.Bakker PART TWO: STATES CORPORATIONS AND THE POTENTIAL FOR TRANSNATIONAL POLITICS Transnational Politics: Political Consciousness, Corporate Power and the Internationalizing State J.Goodman Globalization, Law and Transnational Corporations C.Cutler Internationalization, Business Cleavages and Small Business Activism in Post-war Latin America K.Shadlen Globalization, Political Opposition and the State in Latin America J.Vadi PART THREE: NATIONAL AND TRANSNATIONAL POLICY MAKING Globalization, Internationalization and Change in Eco-Forest Policy in British Columbia S.Bernstein & B.Cashore Domestic Institutions and Non-State Actors in Global Governance: Lessons from the 1979 Protocol Amending the Migratory Birds Convention L.Juillet PART FOUR: SUBNATIONAL INITIATIVES 'Globalist' vs 'Globalized' Cities: Redefining Urban Responses to Globalization P.Smith The Global Economy and the Local State C.Leo PART FIVE: LABOUR AND THE CHALLENGE OF GLOBALIZATION Globalization, Unemployment and the Redistribution of Working Time: Canadian and Comparative Perspectives A.Molloy & J.Shields The Language of Organizing: Trade Union Strategy in International Perspective C.Lipzig-Mumme Here to Stay? The 1998 Australian Waterfront Dispute and its Implications J.Wiseman Index
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".