Bibliographic record
Abstract
In Canada, as elsewhere, the shares for urban areas are larger in the OECD 2020/2021 Survey of Career Guidance for Adults (SCGA) than in the actual population. The percentage of the sample living in urban areas was 87% in Canada, whereas only 82% of the actual Canadian population lives in urban areas. This is likely because people in rural areas tend to participate less in online surveys than those in urban areas, possibly due to lack of access to the internet or digital technologies. shows results from a simple sensitivity analysis where the use of career guidance within urban and rural areas is held fixed, while the share of adults in each group is adjusted to match the population. A weighted average is computed, multiplying the share of adults in each group by their use of career guidance, then summing up across the two groups. The results of the sensitivity analysis show that, all other things being equal, if the regional composition in the sample matched the actual regional composition in the population, the share of adults who used career guidance in the last five years would be 20.0%, negligibly lower than in the sample (20.3%). It suggests that over-representation in urban areas does not have a large impact on the accuracy of the overall findings.
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 | 0.088 | 0.328 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.021 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.097 | 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".