Data Infrastructures in Ecology: An Infrastructure Studies Perspective
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
Abstract The development of information infrastructures that make ecological research data available has increased in recent years, contributing to fundamental changes in ecological research. Science and Technology Studies (STS) and the subfield of Infrastructure Studies, which aims at informing infrastructures’ design, use, and maintenance from a social science point of view, provide conceptual tools for understanding data infrastructures in ecology. This perspective moves away from the language of engineering, with its discourse on physical structures and systems, to use a lexicon more “social” than “technical” to understand data infrastructures in their informational, sociological, and historical dimensions. It takes a holistic approach that addresses not only the needs of ecological research but also the diversity and dynamics of data, data work, and data management. STS research, having focused for some time on studying scientific practices, digital devices, and information systems, is expanding to investigate new kinds of data infrastructures and their interdependencies across the data landscape. In ecology, data sharing and data infrastructures create new responsibilities that require scientists to engage in opportunities to plan, experiment, learn, and reshape data arrangements. STS and Infrastructure Studies scholars are suggesting that ecologists as well as data specialists and social scientists would benefit from active partnerships to ensure the growth of data infrastructures that effectively support scientific investigative processes in the digital era.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.001 | 0.034 |
| Open science | 0.028 | 0.037 |
| Research integrity | 0.000 | 0.003 |
| 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