A Multi-Stakeholder Approach for Leveraging Data Portability to Support Research on the Digital Information Environment
Bibliographic record
Abstract
In this paper, we aim to situate data portability within the evolving discussions of how to support data access for researchers studying the digital information environment.We explore how data donations, enabled by existing data access rights and data portability requirements, provide promising opportunities for supporting research on critical trust and safety topics.Evaluating other data access mechanisms that are more central to policy debates about platform transparency, we argue that data donations are a powerful additional mechanism that offer key legal, ethical, and scientific benefits.We then assess current challenges with using data donations for research and offer recommendations for various stakeholders to better align portability mechanisms with the needs of research.Taken together, we argue that although portability is often considered within a context of competition and user agency, regulators, industry actors, and researchers should understand and leverage portability's potential impact to empower critical research on the societal impacts of digital platforms and services.1.This paper is an expanded version of a chapter included in the compendium for a policy workshop, hosted by the Data Transfer Initative and held in Washington, DC, in February 2024.The compendium can be found here: https://dtinit.org/assets/DTI-Data-Portability-Compendium.pdf.2. Howison, Wiggins, and Crowston (2011) define digital trace data as "records of activity (trace data) undertaken through an online information system (thus, digital)."
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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 | 0.279 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.022 | 0.037 |
| Open science | 0.006 | 0.040 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".