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Record W3199900533 · doi:10.47577/tssj.v23i1.4137

Damage compensation for innocent defendants and Convicts in Iran and the Canadian legal system

2021· article· en· W3199900533 on OpenAlexaboutno aff
Mohammad Kazem Khanjani, Atoosa Bahadori

Bibliographic record

VenueTechnium Social Sciences Journal · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsnot available
Fundersnot available
KeywordsInnocenceConvictCompensation (psychology)Punishment (psychology)LawClearanceCriminal lawCriminologyConstructivePolitical scienceSociologyPsychologyComputer science

Abstract

fetched live from OpenAlex

In the field of innocent defendants and convicts' damage compensation who have endured further losses due to issue criminal supply contracts or orders execution, their innocence has been cleared by issuing acquittance sentences. It counted as one of the most challenging issues in private and criminal law. In these recent years, based on positive changes in the rules of Iran, a lot of works done for innocent defendants and convicts' damage compensation have endured different and unfair punishments. But no integration or constructive work has been done for guiltless convicts' damage compensation who have endured some parts or all their punishments, and their innocence has been proved but not predicted. The reverse of this matter is true in the Canadian law system. Only a guilty convict who has tolerated some or all parts of unfair punishment deserves to receive damage compensation. This study attempted to research the subject's international binding rules, and many practical strategies for guiltless convicts' damage compensation will be considered in both systems by a comparative study.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.052
GPT teacher head0.248
Teacher spread0.197 · 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 designTheoretical or conceptual
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

Citations0
Published2021
Admission routes1
Has abstractyes

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