Bibliographic record
Abstract
I will address the (painful) political and sociocultural context surrounding Marielle Franco’s murder in Brazil, in 2018. Marielle Franco was a Black, lesbian, feminist, activist, mother, partner, politician, and sociologist whose work focused on police violence and militarization. Her assassination constituted both a collective loss and a drastic turning point for those engaged in social struggles and the fight for justice for marginalised populations in Brazil. The attack against Marielle Franco — considered a powerful social symbol — and by extension a direct attack against leftist ways of thinking and the social achievements of Black, feminist, and queer movements in Brazil, can be perceived by some as a lost battle in the fight for equality in Brazil. In the middle of the historical violence that characterizes Brazil, which includes high rates of hate crimes and police brutality that comprises even murders (particularly against Black people), Marielle’s assassination became a sheer message for various social movements to realize what has socially been lost and the high stakes that Brazil faces regarding social equality today. I use Marielle Franco’s murder as an analytical site to disentangle Brazil’s evolving political and sociocultural context. This includes the various stances and social policies developed around gender, race, and sexuality (and their connection to left-wing and right-wing political fields) before the impeachment of President Dilma Rousseff (Worker’s Party) and during the “coup d’etat” that led Michel Temer to power and the election of right-wing President Jair Bolsonaro. I also analyze how Franco’s political life and death (i.e. who did order her assassination?) have encouraged the development of transnational solidarity movements, specifically in Montreal.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.860 | 0.747 |
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".