Open Science, Open Data, and New Opportunities for Cooperative Extension
Bibliographic record
Abstract
The array of problems presented to Extension professionals is broad and growing in number and complexity. As Extension has demonstrated its adaptability in addressing these issues, new ways of working have emerged. Access to an expanding pool of scientific reports and data can potentially provide Extension professionals with the greater tools and knowledge they need to collaboratively engage with their communities. However, a key challenge and possible impediment will be access to rapidly emerging research and data. Open science and open data can broaden the evidence base available to Extension educators, and the emerging field of data science offers new tools to help Extension stakeholders make data-informed decisions. A new data-sharing partnership among Canada, Mexico, and the United States may serve as a model for other countries’ rural advisory services and national Extension systems. Fully implementing this expanded role for Extension will require resources to establish a National Community Learning Network and a national data commons as well as advocacy for open access policies at all levels of government. As abstract as open science and open data may seem to local and regional Extension practitioners, equal access to scientific knowledge and underlying research data is not only imperative for local community engagement but also integral for locally appropriate decision-making. Widening access to research and data directly supports the democratization of science and Extension. Opening scientific research and providing effective access to publicly financed data will become essential platforms for university engagement and Extension. It is critical for Extension professionals to understand the analytic powers and emerging policies that easily-accessed research and data can bring to collaborative community engagement.
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 | 0.139 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.052 |
| Scholarly communication | 0.031 | 0.055 |
| Open science | 0.004 | 0.054 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".