Bibliographic record
Abstract
I was hesitant at first to join the Médecins Sans Frontières (Doctors Without Borders) volunteer group here at McGill. I thought that I would be committing myself to an organisation whose humanitarian actions were solely medical-related and never crossed over to topics of injustice and human rights violations, which as many people do not realise is just as crucial, if not more, than needles and bandages. It is an honourable thing to save lives, but it is a crime to do it with indifference. With aspirations of becoming a doctor myself, I was not ready to promote healing with a mouth shut. Luckily, I soon found out that in addition to providing medical assistance, MSF's main missions are to raise awareness by speaking out, either in private or in public, as witnesses of the plights suffered by populations around the world. As the world's most important independent medical relief organisation, MSF provides assistance in more than 85 countries, in the wake of armed conflicts, civil war, epidemics, chronic refugees situations, natural disasters and famines, while launching awareness campaigns and publicly denouncing acts that violate humanitarian laws. In fact, it is one of the first non-governmental organisations (NGOs) to have combined medicine with activism. Another important feature is its complete independence from all political, religious and economic influences.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.098 | 0.022 |
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".