The Palgrave handbook of prison and the family
Bibliographic record
Abstract
This handbook brings together the international research focussing on prisoners’ families and the impact of imprisonment on them. Under-researched and under-theorised in the realm of scholarship on imprisonment, this handbook encompasses a broad range of original, interdisciplinary and cross-national research. This volume includes the experiences of those from countries often unrepresented in the prisoner’s families’ literature such as Russia, Australia, Israel and Canada. This broad coverage allows readers to consider how prisoners’ families are affected by imprisonment in countries embracing very different penal philosophies; ranging from the hyper-incarceration being experienced in the USA to the less punitive, more welfare-orientated practices under Scandinavian ‘exceptionalism’. \n \nChapters are contributed by scholars from numerous and diverse disciplines ranging from law, nursing, criminology, psychology, human geography, and education studies. Furthermore, contributions span various methodological and epistemological approaches with important contributions from NGOs working in this area at a national and supranational level. The Palgrave Handbook of Prison and the Family makes a significant contribution to knowledge about who prisoners’ families are and what this status means in practice. It also recognises the autonomy and value of prisoners’ families as a research subject in their own right.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".