Oxfam and its Global Campaign on Trade
Bibliographic record
Abstract
Oxfam is typical of present-day NGOs for whom advocacy is a key element in their approach. For most of its early history the organization focused on emergency relief and development assistance projects. It began in Britain in 1942 as the Oxford Committee for Famine Relief, campaigning for food supplies to be sent through an allied naval blockade to starving women and children in enemy-occupied Greece during the Second World War. In the years after the war, it grew to become a major NGO in the fields of emergency relief in disaster situations, and long-term development in poor communities around the world. Other similar organizations associated with the British organization grew up in other Northern countries (e.g. Oxfam Canada), and in 1995, these came together to form the Oxfam International confederation. Today the 13 affiliated organizations of this confederation work in over 100 countries, with more than 3,000 local organizations who strive to help people living in poverty to exercise their human rights, assert their dignity as full citizens and take control of their lives. Oxfam’s efforts are focused on three areas: long-term development programmes aimed at reducing poverty and combating injustice, assistance to people affected by natural disasters or conflict, and advocacy and campaigning to raise awareness of the causes of poverty and press decision makers to change policies and practices that reinforce poverty and injustice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.116 | 0.024 |
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".