Bibliographic record
Abstract
The near impossibility of identifying the common qualities of global organizations probably follows from the fact that their most publicly visible features seem to assemble into contradictions. They tend to be sources of popular prestige and gathering points of NCO activism, yet in the media every major calamity of international relations and global finance becomes a failure of global governance (the examples are too numerous to mention, though Kosovo, Rwanda, Iraq, and Syria come instantly to mind). Many of their goals involve correcting the wrongs of states, yet they are persistently, almost defiantly state-centric; and even with the creation of new norms and the dramatic rise of NCO participation in their initiatives, their decision-making remains dominated by states (Weiss and Daws 2007 = 3). They trumpet their efforts to be transparent and accountable, yet regularly generate documents that heighten obscurity, while producing ideas and policies behind closed doors. They are commonly seen as epicenters of an oppressive neoliberal worlcl order, associated with a dramatically widening global income gap between rich and poor, while being called upon to lead the way in ending hunger, reducing poverty, and promoting development. The list could go on.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.075 | 0.011 |
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".