Policy and Data-Intensive Scientific Discovery in the Beginning of the 21st Century
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent developments in our ability to capture, curate, and analyze data, the field of data-intensive science (DIS), have indeed made these interesting and challenging times for scientific practice as well as policy making in real time. We are confronted with immense datasets that challenge our ability to pool, transfer, analyze, or interpret scientific observations. We have more data available than ever before, yet more questions to be answered as well, and no clear path to answer them. We are excited by the potential for science-based solutions to humankind's problems, yet stymied by the limitations of our current cyberinfrastructure and existing public policies. Importantly, DIS signals a transformation of the hypothesis-driven tradition of science ("first hypothesize, then experiment") to one that is typified by "first experiment, then hypothesize" mode of discovery. Another hallmark of DIS is that it amasses data that are public goods (i.e., creates a "commons") that can further be creatively mined for various applications in different sectors. As such, this calls for a science policy vision that is long term. We herein reflect on how best to approach to policy making at this critical inflection point when DIS applications are being diversified in agriculture, ecology, marine biology, and environmental research internationally. This article outlines the key policy issues and gaps that emerged from the multidisciplinary discussions at the NSF-funded DIS workshop held at the Seattle Children's Research Institute in Seattle, on September 19-20, 2010.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it