Bibliographic record
Abstract
The entrenched and widespread lockstep system of schooling, where students are locked into a year or grade based on age and "step" forward with their age peers, means there is a general perception that single-grade classes are the norm. Yet mixed-grade classes of one sort or another have always been common and typically still account for one quarter to one third of all classes in developed countries and more in developing countries. Such classes are mostly formed by necessity, because of insufficient students (as in remote rural areas), insufficient teachers, or uneven grade enrolments. Some mixed-grade classes, however, are formed by choice. The issue of terminology is particularly pertinent when trying to separate findings for the different types of mixed-grade classes in elementary schools: nongraded, multiage, multigrade, composite, and stage classes. Each has different contextual characteristics that could potentially influence student achievement. Yet characteristics of these classes have traditionally not been well described in research publications; therefore, it is not always possible to clarify which type of class is being studied. Although secondary schools also sometimes have mixed-age, mixed-grade classes (e.g., vertical semester organisation), they are not discussed here.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".