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Record W3206575318 · doi:10.7146/torture.v31i2.128337

Analyzing long-term impacts of counterinsurgency tools in civil wars: A case study of enforced disappearances in Algeria

2021· article· en· W3206575318 on OpenAlexaff
Aïcha Madi

Bibliographic record

VenueTorture Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTorture, Ethics, and Law
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsTransitional justiceHuman rightsPolitical scienceState (computer science)CriminologyCivil societyEconomic JusticeRestorative justiceLawSpanish Civil WarSociologyPolitics

Abstract

fetched live from OpenAlex

Background: The decade long civil war that struck Algeria in the 90s still has strong impacts on the Algerian society today. This article aims at describing how mass human rights violations committed by state actors have repercussions on far more than just the direct victims, which complicates post-war recovery and should be considered during the elaboration of any transitional justice process. Method: Interviews conducted among mothers, brothers and wives of individuals that were taken by state actors and have disappeared since then were analyzed to understand the impact enforced disappearances can have on more than the direct victims. Results: The study of the Algerian experience with enforced disappearances shows that the victimizations that result from enforced disappearances are multi-level and long-term. Discussion: Any post-conflict rehabilitation process that comes after such mass inhuman treatments that aspires to be complete and truly contribute to social well-being needs to also take into consideration secondary level victims, which represent family members of the direct victims, as well as third level victims, which represent the societal and collective memory impacts that result from the use of human rights violation mechanisms by state actors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.376
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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