Bibliographic record
Abstract
I thank the opportunity to reply to Eduardo Siqueira’s and Maria Ines Carsalade Martins’ contributions dealing with my article on global precariousness. In this reply I will discuss the sections of their contributions that deal more specifically with my article. Concurrent with Prof. Siqueira, moving beyond proximal deter-minants to multilevel frameworks that encom-pass the mechanisms linking more than one level is a must for a realist deeper understanding of worker’s health. How political economy and in particular class relations determine patterns of labor market and social protection is palpable in today’s Brazil predicament. Did the Partido dos Trabalhadores – PT (Workers’ Party) have an al-ternative to class compromise to achieve its pop -ulation health gains, or was there a way to avert the breakdown of class compromise once the ef-fects of the global recession reached Brazil? Better data and (mixed) methods are necessary to better understand the social contribution to precarious employment, yet explanatory models are also needed
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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 | 0.042 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.013 | 0.022 |
| 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".