From “Observation Dude” to “An Observational Study”: Gaining Access and Conducting Research Inside a Paramilitary Organization
Bibliographic record
Abstract
Although challenges and barriers to researchers' access are common across any number of empirical sites in socio-legal research—as suggested by the very nature of this special issue and the call for papers that generated it—it has been suggested recently that prisons and other institutions of penal confinement provide a particularly troubling case study. For instance, Loïc Wacquant argues that “ the ethnography of the prison thus went into eclipse at the very moment when it was most urgently needed on both scientific and political grounds.” Although the causes of the decline as understood by Wacquant transcend problems with access (principally, for Wacquant, a transition from the “maternalist (semi-) welfare state to the paternalist penal state”), access, by all accounts, is an ongoing and significant concern. As Kimberly Jacob Arriola summarizes, “conducting research in correctional settings is extremely difficult. Inmates (and any other institutionalized population for that matter) are considered a special population deserving of additional research protections… Moreover, many correctional administrators may not see research as a priority and not want researchers ‘poking around’ for fear that they may discover something less flattering.” Of course, one must be careful not to overstate the paucity of research inside penal institutions, especially given that the decline was probably less severe outside the US, and given that the last half dozen or so years have marked somewhat of a renaissance of scholarship on life inside carceral facilities. Also, the partial decline should not be taken as grounds for lionizing those researchers who do gain/have gained access to prisons and jails to carry out socio-legal research.
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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 | 0.273 | 0.385 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.077 |
| Scholarly communication | 0.026 | 0.028 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".