Chapter 4: COP25 in Madrid
Bibliographic record
Abstract
How can we end our fossil society in a fair way? Changing directionIt is a cool Friday in October.As usual, we are on strike in Mynttorget, when Andrea joins us.She often helps the young people and belongs to the core group among the intergenerational strike organisers."Look at these videos from Chile," she says; that is the country where many of her relatives live.Large parts of the population refuse to accept the enormous social disparities in the country any longer, and thousands are protesting in the streets of Santiago.They want to see political change, a democratisation of their society.At the same time, after strike days with Indigenous people in North America and Canada, Greta and other activists are heading for that same city, Santiago -for the next COP meeting of all countries.The unrest in Chile is increasing every day.The government is intervening with increasing brutality, and goes on to cancel the COP summit.Madrid steps in.And the activists begin their journey back to Europe.Many young activists from Europe travel to Spain.They want to develop the movement, because the official delegations are doing almost nothing: but without international cooperation, oil, coal, and gas will not stay in the ground, and the forests will not remain intact. The global group comes togetherDecember has arrived.Outside in the streets of Madrid, the air is mild.In the huge halls of COP25 (Conference of the Parties), the governments of ev-
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.415 | 0.160 |
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".