Is Spotlighting Enough? Environmental NGOs and the Commission for Environmental Cooperation
Bibliographic record
Abstract
The initial questions of our survey questionnaire focused on whether ENGOs were familiar with the CEC, and if so, how frequently they followed the proceedings of the CEC Tables 1 and 2 clearly illustrate that a substantial majority of respondents were not familiar with the CEC (66.3%) and that only a small percentage of those who were aware of the CEC frequently followed its proceedings (18.5%).5 In short, our survey results indicate that the CEC has a long way to go gain the attention of most ENGOs in both Canada and the United States. A Canadian ENGO leader said, "It's a matter of resources: ENGOs do not have enough to be consistent players." An American ENGO head added, "It doesn't mean there's no interest in the CEC... but there are no resources to be engaged with it." An interesting comparative note from the results listed in Tables 1 and 2 is that while a larger percentage of Canadian ENGOs than United States ENGOs (40.5% to 28.3%) said they were familiar with the CEC, a much higher percentage of United States respondents than Canadian respondents who were familiar with the CEC (30.8% to 7.1%) said they "frequently" followed the proceedings of the CEC. Again, this finding may well be an illustration of the lack of resources available to Canadian ENGOs. Reflecting on this point, a Canadian ENGO member stated "ENGOs are too dependent on Environment Canada for funding... The ENGOs are often put in the position of pushing for governmental environmental initiatives against business and industry. Environment Canada can also use the ENGOs as a counterweight.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".