Bibliographic record
Abstract
In our recent editorial, we suggested that efforts should continue to focus on increasing human papillomavirus (HPV) vaccination coverage in girls. Harper et al. suggest that vaccinating boys would be more effective than targeting girls. Their arguments have important limitations and include factual errors. First, difficulties in obtaining a representative sampled network and unbiased estimates of network structures have previously been shown ( 1 , 2) . The estimated network from Jefferson High, used by Harper et al. to support their conclusion, is no exception. The students at Jefferson High are not representative of students from similar schools ( 3 ). Furthermore, the network is likely to be biased and truncated because it only included three romantic and three nonromantic relationships between students who attended Jefferson High or one middle school and excluded 49% to 61% of all nominated partnerships that were formed outside these schools ( 3 ). Excluding these partners can lead to sex bias because girls tended to be involved with older boys and to report an out-of-school partner.
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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.122 | 0.070 |
| Insufficient payload (model declined to judge) | 0.031 | 0.022 |
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".