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Record W2805392209 · doi:10.5287/ora-azvmxxzxg

Law, poverty and time: the dynamics of poverty in constitutional human rights adjudication

2016· dissertation· en· W2805392209 on OpenAlexaboutno aff
Yishai Mishor

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2016
Typedissertation
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsnot available
Fundersnot available
KeywordsAdjudicationPovertyHuman rightsPolitical scienceLawDynamics (music)Law and economicsSociology

Abstract

fetched live from OpenAlex

Poverty is an event in time. Only dynamic thinking can fully capture its reality. This thesis contends that human rights case law is based on a static perception of poverty inconsistent with the dynamic perception of poverty in economics. Failing to notice its temporal aspects, the examined courts consequently produce judgments that overlook essential aspects of this socio-economic phenomenon. This is puzzling, since in other contexts of constitutional human rights adjudication the passage of time bears a significant role. This means that for courts to switch from a static perspective to a dynamic perspective of poverty does not require new legal tools. The duration of poverty and change in poverty can be incorporated into judicial thinking using familiar norms and doctrines. The extent of poverty, whether it is transitory or a long-term situation, the chances of escaping it in the near future, the fluctuations in depth of poverty over the years, the probability that upon emerging from poverty one will be caught up in it again, the inheritance of poverty from parents to children: these are all time-related concerns that bear profound significance on the lives of poor people. A static examination not only overlooks these issues, but also neglects the essence of long-term poverty. Viewing poverty through the lens of time would reveal a broader and more complex human rights picture, producing a richer legal analysis, and, finally, leading to a more suitable remedy. This study examines cases that consider claims relating to the economic situation of poor people, concentrating on examples from France, Canada and Israel. The analysis reveals the temporal approach of each judgment and suggests an alternative, dynamic reading of poverty.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.741
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.279
Teacher spread0.259 · 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; both teacher heads agree on what is shown here.

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
Published2016
Admission routes1
Has abstractyes

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