Bibliographic record
Abstract
# Does it matter? {#article-title-2} EDITOR—Grech et al seem to confuse statistical significance with practical significance.1 They found highly significant differences in sex ratios among the regions they studied not because of large differences in the sex ratios but because of their large sample sizes. In any case, the use of significance testing in this context is questionable: the purpose of significance testing is to make inferences about a population from a sample, and here whole populations are being studied. What is the population about which Grech et al are trying to make inferences? In all regions that Grech et al studied I would expect 51 boys out of every 100 live births. Dodifferences in sex ratio at the third decimal place and beyond really have any practical significance? 1. ↵1. Grech V, 2. Savona-Ventura C, 3. Vassallo-Agius P .Unexplained differences in sex ratios at birth in Europe and North America.BMJ2002; 324:1010–1011. (27 April.) [OpenUrl][1][FREE Full Text][2] # Latitude has important role {#article-title-4} EDITOR—In their ecological study of 27 countries Grech et al reported a higher male to female ratio at birth for southern Europe than for central and Nordic Europe.1 A reversed latitudinal relation emerged for North America, with Mexico and the United States yielding lower sex ratios than any European country; Canada's sex ratio (although not stated) … [1]: {openurl}?query=rft.jtitle%253DBMJ%26rft.stitle%253DBMJ%26rft.issn%253D0007-1447%26rft.aulast%253DGrech%26rft.auinit1%253DV.%26rft.volume%253D324%26rft.issue%253D7344%26rft.spage%253D1010%26rft.epage%253D1011%26rft.atitle%253DResearch%2Bpointers%253A%2BUnexplained%2Bdifferences%2Bin%2Bsex%2Bratios%2Bat%2Bbirth%2Bin%2BEurope%2Band%2BNorth%2BAmerica%26rft_id%253Dinfo%253Adoi%252F10.1136%252Fbmj.324.7344.1010%26rft_id%253Dinfo%253Apmid%252F11976243%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/ijlink?linkType=FULL&journalCode=bmj&resid=324/7344/1010&atom=%2Fbmj%2F325%2F7359%2F334.2.atom
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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| 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".