Bibliographic record
Abstract
I will be using ProQuest, EBSCOhost, Ovid, PubMed and Web of Science Core Collection (1900 – present), these five well-known online databases for this meta-analytic research. Additionally, in ProQuest, 7 datasets are selected: Education Database (1988 – current); Linguistics and Language Behavior Abstracts (LLBA) (1973 – current); PAIS Index (1914 – current); ProQuest Dissertations & Theses Global; PTSDpubs (1871 – current); Public Health Database; Sociological Abstracts (1952 – current). In EBSCOhost, Academic Search Complete, Academic Search Ultimate, eBook Collection, Medline Complete, MLA Directory of Periodicals and MLA International Bibliography are included. In Ovid, I choose AMED (Allied and Complementary Medicine), APA PsycArticles Full Text, ERIC; APA PsycInfo (from 1967 to May week 4 2020) and APA PsycTests. My access is made possible by library of University of Saskatchewan. The selection of these datasets are based on: (1) their availability; (2) their reputation for storing massive academic resources as used in many other linguistic meta-analytic research; (3) their powerful and easy to use searching and filtering functionalities; (4) their relevance to linguistics; psychology; social science; health and medicine; education etc. that will be related to study of international adoptees' language development and acquisition.
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 | 0.025 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.044 | 0.059 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.400 | 0.105 |
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".