Bibliographic record
Abstract
Abstract The notion of transborder activism has emerged mostly since the beginning of the 1990s, with the growing visibility of advocacy networks and mass protests against neoliberal policies (Seattle 1999; Quebec 2001; Cancun 2003) promoted by states and interstate financial institutions (International Monetary Fund, World Bank), and with the implementation of free trade agreements (North American Free Trade Agreement, World Trade Organization (WTO)). Nonetheless, the nineteenth century anti‐slavery and women's suffrage advocates were active across borders. Their strategies and emphasis on reclaiming rights and dignity also re‐emerged on a regular basis and nurture new forms of cross‐border activism, as with the numerous campaigns and networks for peace, human rights and anti‐apartheid. More recently, United Nations conferences have also fostered activism across borders that simultaneously strengthen, for instance, grassroots feminist groups locally. Transnational feminist networks have indeed been among the early attempts to recognize and value diversity in terms of cultures, races, gender and classes (horizontality and relationality among sectors) as a basis for solidarity and a globalizing praxis that respects local contexts and varied experiences (Conway 2008).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.004 |
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".