Research Commons: Site of Innovation, Experimentation, and Collaboration in Academic Libraries
Bibliographic record
Abstract
Background: This article examines the role the Research Commons plays in supporting digital scholarship in the academic library.Analysis: Relevant literature from library and information science and digital humanities research was reviewed. An environmental scan of select Research Commons and digital scholarship organizations was completed.Conclusion and implications: The Research Commons model encourages interdisciplinary collaboration and takes a holistic approach to providing support services to scholars throughout the research life cycle. The team-based and interdisciplinary nature of digital scholarship production lends itself well to this model. In addition, the training and technology needs associated with digital scholarship align with expertise housed within the library, making the Research Commons a natural point of connection for scholars and librarians engaged in the creation of new modes of scholarly production.
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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 | 0.036 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.015 | 0.047 |
| Scholarly communication | 0.040 | 0.023 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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; 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".