Bibliographic record
Abstract
Jack Blaney’s State of the Basin address outlines the challenge that faces us provincially, nationally, and globally. We must implement sustainable solutions before we fully understand what it takes to achieve sustainability. The remarkable increase in readership of this journal indicates that we are satisfying the urgent demand for cutting-edge information about knowledge-based management of British Columbia’s natural resources. We receive new subscriptions to the print version of JEM every day, and the growth of on-line access is notable. From the second to third quarters of this fiscal year, the number of unique visitors to the JEM website increased 166%, while the number of documents downloaded doubled to over 8000! This is wonderful news for past and prospective authors—articles in JEM reach a rapidly growing audience. It’s also good news for managers and stakeholders who will benefit as the flow of informationintensifies and the dialogue in JEM expands. Watch for a new Reader Response feature in our next print issue—this section will be your opportunity to contribute to this important dialogue, and to play your part in responding to Jack Blaney’s challenge. Let’s build a better future now as we wait for the definition of “sustainability” to crystallize.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".