Findings from Environment and Climate Change Canada's open science engagements: Identified user needs
Bibliographic record
Abstract
Canada’s 2018-2020 National Action Plan on Open Government (2018-2020 NAP) provides Environment and Climate Change Canada (ECCC) with a mandate to “promote open science and actively solicit feedback from Canadians and federal scientists on their needs with respect to open data and open science” (open science milestone 5.4). To deliver on this commitment, the 2018-2020 NAP proposes two steps: 1) holding 10 open science engagement sessions with federal scientists and invited Canadians across the country, and; 2) publishing a report on identified user needs. Between March 12, 2019 and February 26, 2021, ECCC has investigated the open science needs of federal open science users through direct engagements with the public as well as with federal scientists. This report summarizes ECCC’s findings and fulfills Milestone 5.4 of the 2018-2020 NAP.
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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 | 0.027 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.024 | 0.005 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".