Bibliographic record
Abstract
<p>The primary objective of the Canadian Income Survey (CIS) is to provide information on the income and income sources of Canadians, along with their individual and household characteristics. The data collected in the CIS is combined with Labour Force Survey (LFS, record number 3701) and tax data.</p> <p>The survey gathers information on labour market activity, school attendance, disability, unmet health care needs, support payments, child care expenses, inter-household transfers, personal income, food security, and characteristics and costs of housing. This content is supplemented with information on individual and household characteristics (e.g. age, educational attainment, main job characteristics, family type), as well as geographic details (e.g. province/territory, census metropolitan area (CMA)) from the LFS. Tax data for income and income sources are also combined with the survey data.</p> <p>Results from the survey are made available not only to various levels of government, but also to individuals and organizations. All levels of government can use CIS data to shape policies and programs related to the economic well-being of Canadians. Statistical organizations such as the Organization for Economic Cooperation and Development (OECD) use the results for international benchmarking and comparison studies.</p>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".