Framing Political Change: Can a Left Populism Disrupt the Rise of the Reactionary Right? Comment on "Politics, Power, Poverty and Global Health: Systems and Frames"
Bibliographic record
Abstract
Solomon Benatar offers an important critique of the limited frame that sets the boundaries of much of what is referred to as 'global health.' In placing his comments within a criticism of increasing poverty (or certainly income and wealth inequalities) and the decline in our environmental commons, he locates our health inequities within the pathology of our present global economy. In that respect it is a companion piece to an editorial I published around the same time. Both Benatar's and my paralleling arguments take on a new urgency in the wake of the US presidential election. Although not a uniquely American event (the xenophobic right has been making inroads in many parts of the world), the degree of vitriol expressed by the President-elect of the world's (still) most powerful and militarized country is being used to further legitimate the policies of right-extremist parties in Europe while providing additional justification for the increasingly autocratic politics of leaders (elected or otherwise) in many other of the world's nations. To challenge right-populism's rejection of the predatory inequalities that 4 years of (neo)-liberal globalization have created demands strong and sustained left populism built, in part, on the ecocentric frame advocated by Benatar.
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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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.031 | 0.043 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".