Bibliographic record
Abstract
Following two poster presentations during iAssist Conferences in Toronto, 2014, Working Across Boundaries: Public and Private domains and in Bergen 2016, Working Across Boundaries: Public and Private domains – Part 2, we present now a third poster - Working Across Boundaries: Public and Private D omains - Part 3 . The poster will present the results of a follow-up survey that we plan to realize in Turin in Fall. The main argument of the survey will be WHY? It seems that every effort done in order to organize, disseminate and make the data usable in Turin (and in Italy as well) would be unsuccessful, WHY? The survey topics will include items like: agency type (public or not, dimension, services offered,...), data type and volume, how and when they are used, for which purpose, the actual mode and tools used to conserve data, the archives type, the accessibility level and so on. The work is aimed to identify the reasons that withstand the organization of efficient data archives in order to better promote their use
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 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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.014 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".