Globalizing assessment frameworks: Centring gender and culture in assessment for women on probation in Pakistan
Bibliographic record
Abstract
In 2019, legislation was passed in Punjab, Pakistan, mandating the use of risk/need assessment tools. Subsequently, the Probation Service Assessment Planner (PSAP; Brown and Bhutta, 2021), a ground-up gender and culturally informed assessment tool and case management planner, was developed and piloted in a sample of 45 women on probation in Pakistan. Participants reported culturally influenced and gendered needs, including relational challenges and a lack of access to education and employment, as well as gender neutral needs. Most PSAP domains had acceptable to excellent internal consistency (α = 0.58−0.90) and assessors broadly discriminated well between priority and non-priority needs. While the PSAP is a promising assessment tool, future research must further evaluate the measure’s psychometric validity and create a framework for applying the PSAP in Pakistan’s resource-scarce correctional system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".