Bibliographic record
Abstract
In my previous book, Human on the Inside, I talked about the nature of systems, how human beings need systems to frame how we live and interact and how at the same time these systems dehumanize us, turning us into mere customers, clients, inmates, files, bar codes, or pixels on a screen.In that book, my focus was the prison system in Canada, but all systems-whether it's marriage, religion, criminal justice, income tax, education, health care, you name it-embody the same principle.For us to be safe and healthy, whether as a community or as individuals, we need to find a balance between connecting with each other as persons and relying on systems that treat us as objects.This person-object tension is central to the life of everyone who cares for children who have to live separate from their parents, and, of course, to the lives of the children themselves.When a baby is born to an abusive mother or an addicted or runaway father-to pick one examplewho cares for the baby?The law says that if I know a child is being abused, I'm obligated to report it.But if I'm the grandparent, and reporting the abuse means a
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.535 | 0.433 |
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".