Review of Networks of Trust: The Social Costs of College and What We Can Do About Them, by A. S. Laden
Bibliographic record
Abstract
It's burnt.""What?" "So are the Chekhov books you lent me.Denny found out I was on the pill, he's burnt all me books.""Oh, Christ.I'm sorry, I'll get you some more books.""Oh, sod the books.I wasn't referring to the books.Why can't he just let me get on with me learning?You'd think I was having an affair, the way he behaves.""Perhaps you are having an affair.""Go 'way, I'm not!What time have I got for an affair?Jesus, I'm busy enough finding meself, let alone finding anyone else.I'm beginning to find me.It's great.It is, you know, Frank.It might sound selfish but all I want for now is what I'm finding inside me." ~From the screenplay "Educating Rita" (Gilbert, 1983)Rita's crisis stems from the loss of friends, family, and community in the name of education-or is it indoctrination?The review of Networks of Trust that follows can be seen as five essays on the alienating effects of school and college education with thoughts on how to mitigate these consequences.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".