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Record W2479486318 · doi:10.1017/cbo9780511818783.006

The early judges, legal specialists and the search for religious authority

2004· book-chapter· en· W2479486318 on OpenAlexaff
Wael B. Hallaq

Bibliographic record

VenueCambridge University Press eBooks · 2004
Typebook-chapter
Languageen
FieldSocial Sciences
TopicAmerican Constitutional Law and Politics
Canadian institutionsMcGill University
Fundersnot available
KeywordsPolitical scienceLawPsychology

Abstract

fetched live from OpenAlex

THE EARLY JUDGES In the previous chapter, we saw that the proto- qāḍī 's office was not limited to resolving legal disputes and that it involved other activities related to tribal arbitration, financial administration, story-telling and policing. These were normative functions in qāḍī s' appointments down to the 80s/700s and even 90s/710s. Whatever change this office subsequently underwent was by no means sudden. From the ninth decade of the Hijra, the qāḍī 's office increasingly was limited to conflict resolution and legal administration. From this point on, some qāḍī s were appointed qua qāḍī s, with no explicit stipulation of other duties that they should undertake. In fact, the distinctness of these duties and functions was made obvious by the nature of appointments. Thus, when ʿAbd al-Raḥmān al-Jayshānī was dismissed from his function as judge of Egypt sometime during the 130s/750s, he was immediately reappointed there as a tax-collector. The expansion and growing complexity of state functions appear to have required a narrowing down of the duties assigned to officials. However, these appointments seem to have been relatively few in number and for a few decades thereafter many judges continued to combine this office with other functions. The centralization of Umayyad legal administration appears to have begun during the last years of the first century h , a policy that marked a change in the nature of judicial appointments.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.017
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.026
GPT teacher head0.252
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2004
Admission routes1
Has abstractyes

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