Bibliographic record
Abstract
It’s a strange thing watching someone ‘known’ to us for over a decade get a hefty term of imprisonment. Chris was in his mid-twenties and had just received 14 years for a series of violent crimes committed shortly after his last release. It took the State over four years to resolve his matters — an exceedingly long time on remand by anyone’s estimation. The sentence brought Chris a degree of closure but also new opportunities to sink deeper into the mire of prison life. He’ll be well into his thirties before having any chance of making parole. It’s strange also, when, as researchers, we were able to see the train wreck coming but powerless to avert the impending damage. Perhaps, in the tradition of positivist detachment from the field, one has no quarter to try and influence the trajectories of those we study. Still, there can be no denying the substantive emotional investment tied to prison and post-release research (see Liebling 1999; Bosworth et al. 2005; but also Campbell 2002). The ‘field’ — be it policing, courts, prisons, the street more generally — is populated by countless affective moments. Courtrooms, in particular, are a haven for extreme emotional turbulence (Freiberg 2001). There, even the prosecution team agreed Chris had one of the most troubled and deprived early life-courses they had encountered. Still, he had to pay for what he’d done. He had to pay even though it was broadly acknowledged that his was a life bereft of the building blocks necessary for carving out any semblance of a conventional existence. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.560 | 0.364 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".