Bibliographic record
Abstract
An extensive literature search of both peer-reviewed and grey literature databases will be performed to identify relevant information about the topic. A concept plan was developed in close collaboration with a librarian. This plan includes the following concepts: “childcare services” and “children with disabilities”. Databases relevant to our study topic were also identified (i.e., Medline (Ovid), CINAHL (EBSCO), ERIC (EBSCO), Web of Science, PSYCInfo (Ovid), Academic Search and Education Source (EBSCO)), and appropriate keywords were identified. Keywords and writing rules (e.g., truncation, quotation marks, Boolean operators) were adapted to each database. Our search strategies are attached. The librarian has searched the databases in May 2023. The data will be imported from the databases in Endnote reference management software. Then, references will be exported to Covidence systematic review software (www.covidence.org), where duplicates will be removed automatically based on the title of the references.
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 | 0.023 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.116 | 0.075 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.187 | 0.038 |
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".