Foundations for On-Campus Open Social Scholarship Activities
Bibliographic record
Abstract
Social knowledge creation, citizen scholarship, interdisciplinary collaborations, and university-community partnerships have become more common and more visible in contemporary academia. The Electronic Textual Cultures Lab (ETCL) currently focuses on how to engage with such transformations in knowledge creation. In this paper we survey the intellectual foundation of social knowledge creation and major initiatives undertaken to pursue and enact this research in the ETCL. “Social Knowledge Creation: Three Annotated Bibliographies” (Arbuckle, Belojevic, Hiebert, Siemens, et al. 2014), and an updated iteration, “An Annotated Bibliography on Social Knowledge Creation,” (Arbuckle, El Hajj, El Khatib, Seatter, Siemens, et al, 2017), explore how academics collaborate to create knowledge, and how social knowledge creation can bridge the real or perceived gap between the academy and the public. This knowledgebase lays the foundation for the “Open Social Scholarship Annotated Bibliography” (El Hajj, El Khatib, Leibel, Seatter, et al. 2019), which draws on research that adopts and propagates social knowledge creation ideals and explores trends such as accessible research development and dissemination. Using these annotated bibliographies as a theoretical foundation for action, the ETCL began test-driving open social scholarship initiatives with the launch of the Open Knowledge Practicum (OKP). The OKP invites members of the community and the university to pursue their own research in the ETCL. Research output is published in open, public venues. Overall, we aim to acknowledge the expanding, social nature of knowledge production, and to detail how the ETCL utilizes in-person interaction and the digital medium to facilitate open social scholarship.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".