Bibliographic record
Abstract
Public concern for the environment and endangered species is growing. Canadian society has a more involved relationship with nature and natural resources than we did 50, or even 25 years ago. Ironically, this explosion of ecological awareness comes precisely at a time when governments at all levels are scaling back on their involvement in monitoring the environment. Monitoring programs funded through incremental or non-base budgets, combined with the steady pace of government ministry reorganizations, often result in short-term, fragmented, and ineffective government ecological monitoring. In a new phenomenon known as community-based ecosystem monitoring (CBEM), citizen groups, non-government organizations (NGOs), and individual citizens monitor a local species, ecosystem, or ecosystem process. CBEM can be viewed as government downloading of costs or as an historic taking-back of social responsibility. Benefits of CBEM include data acquisition, increased public awareness of nature and ecosystems, and opportunities for environmentalists to see decision-making first-hand. British Columbia is fertile ground for CBEM in that it has a well-developed NGO community, a stunning variety of ecological and natural resource issues, and a government that is currently downsizing its “dirt ministries.” CBEM has a long-established precedent in the First Nations tradition of close and daily observation of nature.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".