Faction in Movement: The Impact of Inclusivity on the Anti‐Globalization Movement<sup>*</sup>
Bibliographic record
Abstract
Objectives.This research examines and evaluates the reasons why the anti‐globalization movement has yet to make significant progress in achieving its primary goals of democratizing international trade negotiation processes. Methods.Data on anti‐globalization protest cycles were collected from news sources for a time period of up to one month that encapsulated the protest events. From these, I constructed brief case narratives of the major events in Seattle, Washington, DC, Prague, Quebec City, Genoa, and Doha to illustrate my argument. Results.I find that the democratic master frame employed by the movement results in two important limitations: (1) the movement cannot exclude participants without undermining its legitimacy, and (2) its lack of a centralized organizational framework makes it impossible to police the actions of participants during major protests. Recognizing these limitations, states hosting global economic summits have demonstrated increasingly a willingness to utilize repressive measures against the movement thereby undermining the movement's ability to achieve its goals. Conclusion.I conclude that the inability of the anti‐globalization movement to exclude violent participants will continue to limit its effectiveness given states' increasing willingness to employ repressive tactics indiscriminately.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".