Bibliographic record
Abstract
BACKGROUND: A selective approach may be used in an ecological study where the aim is to choose a subset of units of analysis (UAs) and produce interpretations about a population of interest (PI) based solely on those UAs. The results for the PI will be reliable if that population is concentrated in the selected UAs and rare in other UAs. This article presents a graphical tool that helps determine whether these conditions are satisfied. DATA AND METHODS: Data on the Inuit and Métis ancestry populations from the 1996 Census of Canada are used for illustrative purposes. Based on a classification statistics table, a concentration-coverage curve can be created for a given PI. The shape of the curve indicates whether it is possible to choose a threshold that will yield both adequate concentration and adequate coverage of the PI. RESULTS: The concentration-coverage curve shows that, among Aboriginal peoples living in rural areas, the Inuit population is classifiable, but the Metis population is not. INTERPRETATION: This method can be applied to any ecological study focussing on the proportion of individuals sharing a single characteristic defined by a binary variable.
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 | 0.098 | 0.382 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.034 | 0.022 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".