An Innovative Way of Finding Best or Least Matching Pairs and Groups
Bibliographic record
Abstract
The procedure introduced in this article is an innovative way for finding the best and least matching pairs. The method can also be extended to find the most converging or diverging groups if the objectives of studies necessitate so. The procedure employs three pieces of information to find out which pairs or groups of subjects make the most or least converging pairs or groups. These three pieces of information are the total differences between pairs of subjects’ responses to the questions in a questionnaire, their place on the continuum defined for the variable under study, and the correlations between pairs of students’ scores. A rank is assigned to each pair with regard to each source of information. These values are then added and the subjects are ranked from the most converging to the most diverging pairs with small and big numbers representing converging and diverging pairs, respectively. After pairing subjects, it is easy to find the most converging or diverging groups by dividing the arranged pairs vertically or horizontally. The procedure is felt to be applicable to many quantitative non-experimental and qualitative studies.
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 | 0.029 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".