ENVIRONMENTAL SURVEYS AND INDICATORS - CONCEPTUAL AND METHODOLOGICAL CHALLENGES
Bibliographic record
Abstract
Statistics Canada has recently embarked on an expansion of its environmental statistics program that will see more than a doubling of the number of surveys and an increase in the output of derived statistical products such as indicators and accounts. For the first time, the agency will begin regular collection of environmental information directly from households. It is also proposing a significant foray into the collection of pollution emissions data from businesses. New indicators of environmental sustainability are already being produced jointly with Environment Canada and Health Canada. All of this work requires new thinking about the concepts and methods that underpin environment statistics. This paper will explore some of the more novel aspects of this new thinking.
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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 | 0.343 | 0.509 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.015 | 0.028 |
| Science and technology studies | 0.006 | 0.055 |
| Scholarly communication | 0.026 | 0.043 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".