The Use of Offer and Acceptance and their Commissive Implication in the Sulha Tribunal
Bibliographic record
Abstract
This article focuses on the analysis of enactment of speech acts of offer and acceptance and their commissive effects in carrying out Sulha informal legal processes. Sulha is a method of resolving disputes used in the Middle East. These processes of Sulha are understood to operate within traditions set by communities that use the process in solving disputes. Just as formal legal processes, the success of a Sulha process is dependent on legal performative of a language used to carry out Sulha tribunals. This is based on the fact that it is through language that informal legal acts are enacted. The study is grounded on the Jordanian Bedouin dialect used in conducting Sulha tribunals whose translation equivalences are given in English. Data are collected through audio-recording which is backed up with note-taking. The audio-recorded data are then played back to identify the speech acts of offer and acceptance. The identified acts of offer and acceptance are then analyzed within the framework of Searle’s (1979) classification of speech acts. In terms of methodology, the study adopts descriptive research design whereby the speech acts of offer and acceptance are described as they occur in the legal discourses used in the informal legal process, Sulha.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".