Nullius in Verba1: Advancing Data Transparency in Industrial Ecology
Bibliographic record
Abstract
Summary With the growth of the field of industrial ecology (IE), research and results have increased significantly leading to a desire for better utilization of the accumulated data in more sophisticated analyses. This implies the need for greater transparency, accessibility, and reusability of IE data, paralleling the considerable momentum throughout the sciences. The Data Transparency Task Force (DTTF) was convened by the governing council of the International Society for Industrial Ecology in late 2016 to propose best‐practice guidelines and incentives for sharing data. In this article, the members of the DTTF present an overview of developments toward transparent and accessible data within the IE community and more broadly. We argue that increased transparency, accessibility, and reusability of IE data will enhance IE research by enabling more detailed and reproducible research, and also facilitate meta‐analyses. These benefits will make the results of IE work more timely. They will enable independent verification of results, thus increasing their credibility and quality. They will also make the uptake of IE research results easier within IE and in other fields as well as by decision makers and sustainability practitioners, thus increasing the overall relevance and impact of the field. Here, we present two initial actions intended to advance these goals: (1) a minimum publication requirement for IE research to be adopted by the Journal of Industrial Ecology ; and (2) a system of optional data openness badges rewarding journal articles that contain transparent and accessible data. These actions will help the IE community to move toward data transparency and accessibility. We close with a discussion of potential future initiatives that could build on the minimum requirements and the data openness badge system.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".