Are You Falling Down on the Job? Do You Need a Four-Legged Teacher?
Bibliographic record
Abstract
Teaching children is challenging, satisfying and sometimes infuriating. It is easier if you have some device for catching their attention. A teaching dog works like a charm. Children are taught by people every day. To be taught by a dog is different and fascinating. They are enthralled and all ears. Teaching an intelligent dog to teach fire safety is not difficult—nor even very time consuming—10 minutes a day will do it. I have had trained dogs teaching fire safety principles to children (and adults) since 1984. The beauty of it is that kids remember what they are taught. We know, because they tell their next year's teachers all about it. “Uncle Sam,” a black Labrador Retriever sired by a police dog, and “Aunt Samantha,” whose ancestry would never have got her into the Colonial Dames, gained many speaking engagements for us that might not have come our way otherwise. Uncle Sam taught five fire safety principles. Samantha mastered and taught six. Samantha gave her first public performance before a group of veterans in Randolph, Massachusetts, at 13 weeks and was letter perfect.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.020 | 0.033 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".