Bibliographic record
Abstract
Abstract How data on individuals are gathered, analyzed, and stored remains largely ungoverned at both domestic and global levels. We address the unique governance problem posed by digital data to provide a framework for understanding why data governance remains elusive. Data are easily transferable and replicable, making them a useful tool. But this characteristic creates massive governance problems for all of us who want to have some agency and choice over how (or if) our data are collected and used. Moreover, data are co‐created: individuals are the object from which data are culled by an interested party. Yet, any data point has a marginal value of close to zero and thus individuals have little bargaining power when it comes to negotiating with data collectors. Relatedly, data follow the rule of winner take all—the parties that have the most can leverage that data for greater accuracy and utility, leading to natural oligopolies. Finally, data's value lies in combination with proprietary algorithms that analyze and predict the patterns. Given these characteristics, private governance solutions are ineffective. Public solutions will also likely be insufficient. The imbalance in market power between platforms that collect data and individuals will be reproduced in the political sphere. We conclude that some form of collective data governance is required. We examine the challenges to the data governance by looking a public effort, the EU's General Data Protection Regulation, a private effort, Apple's “privacy nutrition labels” in their App Store, and a collective effort, the First Nations Information Governance Centre in Canada.
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 | 0.032 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".