Bibliographic record
Abstract
Abstract In many studies the twinning rate, being strongly dependent on maternal age (and parity), has been standardised according to the maternal age distribution. The direct method requires very informative twinning data for the target population. The indirect method is used when the data for the target population is not sufficiently informative or when the target population is small. We have earlier introduced an alternative indirect technique for standardising the twinning rate. Our technique requires even less of the twinning data. Besides maternal age, parity is an influential factor, and should, if possible, be taken into account. In this study we present the traditional standardisation methods based on both maternal age and parity, we propose a new direct standardisation method and we develop our standardisation methods so that they take into account both maternal age and parity. We apply these standardisation methods to data from Finland, 1953–1964, from St. Petersburg, Russia, 1882–92, from Canada 1952–1967, and from Denmark, 1896–1967. These methods all give results very similar to those for the Finnish data, but the effect of parity is strongest with the direct methods. This may be due to the fact that, among extramarital maternities, parity has a strongly increasing effect on the twinning rate. This may be attributed to a higher reproduction capacity among unmarried mothers. Standardisations of the Canadian and the Danish data also give reliable results. With the St. Petersburg data, however, the different standardisations show notable discrepancies. These discrepancies are compared with Allen’s findings.
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 | 0.067 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".