An analysis of penal law objectives in child abuse cases: a comparison of the Kenyan and Canadian experiences
Bibliographic record
Abstract
The main objective of this paper is to assess the veracity of penal law objectives in child abuse cases in Kenya. The Children's Act under Section 2 looks at child abuse as consisting physical, sexual, psychological and mental injury. The research endeavours to cover the various forms of child abuse covered by various pieces of legislation, which are sexual abuse, child physical abuse and child trafficking and exploitation. The area is of interest due to a significant rise in child abuse incidences in the country over the years. One of the objectives of laws is to bring about social order in its purest form with an end to avoid conflict. As such to maintain this order the characteristic response of Kenyan legislate:·" to these atrocities was to pass laws that gave unlimited judicial discretion in sentencing and also severe deterrent punishments. From collected data over a span of 10 years since the enforcement of key legislations, the trend of child abuse crimes across the board have increased in conviction rates, prosecutions and arrests. The question then is whether the objective of the laws is responsible for this inefficiency. To figure out the whether a nexus exists thejurisdiction of Canada was used as a comparative study. The Canadian criminal system, based on Common law, is similar to that of Kenya. The similarity of judicial systems and similar child protection laws provided a proper specimen to analyse whether a nexus exists. The sentencing objectives in child abuse cases however is based on a mixture of retribution and deterrence tempered by mandatory minimum sentence . Across the board it was seen .that the cases of child abuse either diminished or maintained the same number due to effective investigations, implementation of mandatory minimum sentences and less judicial discretion in sentencing. Therefore, the Kenyan experience can be remedied through introduction of mandatory minimums of penalties. This removes discretion of the judiciary and as such aligns the purpose of the laws. Strengthening of child abuse investigations by the Kenya Police Service. This goes to improvement of infrastructure and reduction of bureaucracy. Setting up of proper reporting mechanisms to ensure an associated effort to ensure prosecution of crimes. This goes to the front of accessibility to proper authorities and access to justice.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".