Follow your papers around the world: Using the Digital Commons Dashboard
Bibliographic record
Abstract
Open Access Week Event: “Follow your papers around the world”: Using the Digital Commons Dashboard The Dashboard that is available to all Digital Commons depositors can show where and how often your papers are seen and used. More than half of our readership is international, so it is fascinating to see the global impact your work may be having. Open access scholarship is critically important for researchers and students in the Southern Hemisphere and remote locales. The Dashboard can confirm how great an interest exists in your work, beyond your immediate campus or community. Offering geolocation information down to the village level, the Dashboard shows how information travels, persists, and finds audiences you may not even have dreamed of. Repository Manager Paul Royster will lead the session and demonstrate some of the capabilities of this unique technology--one that publishers and other repository platforms do not feature. When: Monday, October 24, 2022 12:00 PM-1:00 PM (UTC-06:00) Central Time (US & Canada). Zoom Meeting URL:
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 | 0.013 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.036 | 0.024 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.488 | 0.459 |
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".