Bibliographic record
Abstract
As this issue of JCRI goes to press, Israel's onslaught on the people of Gaza is well into its third month, with almost 20,000 Palestinian people dead and starvation a present reality rather than a looming threat. The leadership of the vast majority of nations have been dragged, however reluctantly, by the force of a global protest movement to vote favour of a ceasefire in a non-binding motion of the UN General Assembly, with the single and predictable exceptions of the United States and the UK, two of the staunchest supporters of Israel's settler colonial genocidal project. Meanwhile, the Islamic Republic of Iran benefits from the shift of global attention away from their violent repression of Kurdish and Iranian women, students, and protesters, 15 months since the police murder of Jina Mahsa Amani, and Assad's Syria resumes its practice of what Abu-Hatoum and Ghazzawi describe as Samoud-washing 1 , finding in Israel's massacres convenient cover for their own. Meanwhile in Somalia, Sudan, The Democratic Republic of Congo, and Myanmar, among others, civil wars and authoritarian regimes drive humanitarian crises that go largely unremarked upon in North American mainstream media, while social media algorithmic bias 2 , mainstream media, and state actors work to repress the representation as well as the activity of anticolonial struggle, protest, and solidarity from Turtle Island to Palestine and beyond.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.193 | 0.119 |
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".