A hermeneutic dialogical understanding of data reuse across different access regimes
Bibliographic record
Abstract
Policy and scholarly discourse emphasizing the panacea of Open (research) Data shapes expectations, and directs and legitimizes investments in data technologies and infrastructures. This is driven by the hope that Open Data will quicken the pace of research and innovation through data reuse, and that they do so more effectively than other access regimes, such as stewarded and proprietary data. Drawing on Leonelli’s relational framework and Gadamer’s hermeneutical conceptualization of a horizon of meanings, data reuse can be understood as a fitting process. In the latter, a researcher engages in a hermeneutical dialogical interaction with the data’s affordances with the goal of making a scientific contribution. Moreover, the fitting process takes place within a researcher’s bounded individual horizon (BIH), defined as an intentional orientation towards the future; it is made up of the relations and circumstances that modulate each researcher’s unique situation. Seen thus, data reuse is likely to result from the persistence of a researcher’s desire or need to make a scientific contribution, independently of the data access regime. What is more, the necessary interaction between potential reusers and data curators or owners can open up the interpretive affordances of data in the context of proprietary and stewarded data, making data more mutable compared to the relative immutability of data in open repositories. Accordingly, stewarded data, with the proper curation and digital preservation services, might provide a more sustainable form of sharing and reusing data where privacy is at stake.
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 | 0.060 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.013 | 0.114 |
| Scholarly communication | 0.034 | 0.051 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".