Bibliographic record
Abstract
Objectives and ApproachThe effects of the timing and duration of economic deprivation and maternal mental health on intellectual development is researched using unique linkable databases following almost 90 thousand children born in the Canadian province of Manitoba. This paper studies such questions as: How important are differences in measurement of poverty in assessing intellectual development? How do various differences change over childhood? ResultsConcentrating on those born in 2000-2002 generated a total of 7,424 children having scores on all six measures of intellectual development. Such person-specific information controlled for many individual factors and extended from ages 5 through 17. Major differences were found in the scores associated with exposure to the two kinds of poverty and to poor maternal mental health. In summary, differences emerge by age 5, with administration of the Early Development Index. These differences are largely the same at age 8, even though another measure (the Grade 3 Competencies Index) is used. ConclusionsIf household poverty is taken as a definition of poverty, poverty seems much more important than maternal mental health in affecting a child’s intellectual development. However, if neighborhood poverty is used to define poverty, maternal mental health and poverty have effects on childhood intellectual development which are more similar. Definition plays a critical role in interpretation. ImplicationsThese population-based data, with multiple measures and multiple time points, suggest many analytical possibilities. If childhood conditions are included, the list of medical conditions which might correlate with intellectual development multiplies.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".