Bibliographic record
Abstract
Recently I received a cause invitation from an old school friend on Facebook. The cause was to support micro-chipping of all paedophiles. I rejected that invitation. All I could think of was the microchip embedded in my Labrador. The way I understand that chip to work is that if someone finds my dog wandering the streets they can take him to a vet or the RSPCA who will then scan the chip and return him to me. The obvious question seemed to be: how would such a measure help protect children? After all, protection of children is the goal articulated in the mission statement of this cause.1 Preliminary investigation revealed that the type of chip anticipated to be implanted in paedophiles is not the same as those in animals. It is a satellite-tracking device that would be used to monitor the person’s movements. It is suggested that the implantation would occur before the offender is
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 | 0.026 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.081 | 0.061 |
| Insufficient payload (model declined to judge) | 0.066 | 0.020 |
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".