INTERNATIONAL CONFEDERATION OF FREE TRADE UNIONS AFRICAN REGIONAL ORGANISATION TRADE UNIONS AND POVERTY ALLEVIATION: TOWARDS A COMPREHENSIVE STRATEGY
Bibliographic record
Abstract
Despite all our technological breakthroughs, we still live in a world where a fifth of the developing world’s population goes hungry every night, a quarter lacks access to even the basic necessities, like safe drinking water, and a third live in abject poverty. UNDP 1994. Over the past half-century, International Financial Institutions (IFIs) have emerged as major forces in directing the evolution of the global economy. Backed by the wealthy industrial countries, the IFIs, particularly the World Bank and the International Monetary Fund (IMF) have used their development financing and lending activities to influence the policies of developing country governments. For most of their history, the IMF and the World Bank have made development and financial assistance conditional on the implementation of Structural Adjustment Programmes (SAPs) that were supposed to promote economic growth. But many of the measures included in the SAPs, such as government downsizing, privatisation, and deregulation of labour markets, had negative effects on the lives of poor and
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 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".