UAPS - Non-Aboriginal Survey - SPSS - FOR LICENSE
Bibliographic record
Abstract
This survey consists of telephone interviews conducted with a representative sample of 2,501 non-Aboriginal people (aged 18 and older) living in 10 of the cities covered by the main study (excluding Ottawa) (250 per city). Interviewing took place between April 28 and May 15, 2009. The margin of error for a probability sample of 2,501 is plus or minus 2.0 percentage points, 19 times in 20. (Because the sample for the main survey is based on individuals who initially "self-selected" for participation, no estimate of sampling error can be calculated for the main survey. It should be noted that all surveys, whether or not they use probability sampling, are subject to multiple sources of error, including but not limited to sampling error, coverage error and measurement error.) (From p. 22 http://uaps.ca/wp-content/uploads/2010/03/UAPS-Main-Report_Dec.pdf) \n \nData View tab contains 2501 rows of data. \n \nVariable View tab contains complete data dictionary. \n \nThe questionnaire used in these interviews is available at http://uaps.ca/wp-content/uploads/2010/04/UAPS-Non-Aboriginal-Survey-Questionnaire-FINAL-ENGLISH.pdf
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.226 | 0.085 |
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".