Medición funcional en el campo de la Ética en Política
Bibliographic record
Abstract
We present, in a synthetic way, some of the main findings from ten studies that were conducted in the field of ethics in politics, using the Functional Measurement framework. These studies were about (a) Angolan and Mozambican people’s views about the legitimacy of military-humanitarian interventions, (b) French people’s perspectives regarding the government’s responsibility for the health of consumers of illicit substances, (c) Togolese people’s views about the acceptability of political amnesties in a time of political transition, (d) the perspective of victims of the genocide of the Tutsis in Rwanda regarding the attribution of guilt by association to offspring of perpetrators, (e) slave descendants’ views about the acceptability of national policies on reparations for slavery, (f) Colombian people’s willingness to forgive perpetrators of violence who harmed family members during the civil war, (g) the attitudes of French and Colombian people about national drug control policies, (h) Indian students’ views about the appropriateness of the death penalty for murder or rape, (i) Colombian people’s perspectives regarding corruption, and finally (j) Venezuelan people’s conceptualization of human rights. The main findings are discussed in reference to six of the foundations of Moral Foundations Theory.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".