Humanities scholars’ needs for open social scholarship platforms as online scholarly information sharing infrastructure
Bibliographic record
Abstract
The contemporary scholarly communication environment is characterized by the growth in mandates and infrastructure for open access publication and open approaches to the research lifecycle, with a consequent explosion in the number of online platforms seeking to provide infrastructure for open scholarship. These include corporate academic social networks and scholar-governed infrastructure created as a reaction against those networks, as well as the recent major transformation of the social media landscape in the wake of changes at Twitter (now X), previously a major outlet for scholarly engagement with the public. Analysts of this environment have pointed out that most platform initiatives focus on narrow use cases rather than building up solutions through a holistic understanding of scholar workflows. This exploratory study uses focus group interviews to draw out responses to one academically governed platform, the Humanities and Social Sciences (HSS) Commons, in the context of humanities scholars’ existing work. It explores humanities scholars’ needs and behaviors related to sharing scholarly information with each other and broader audiences, particularly on the Internet. Feedback from participants sheds light on opportunities and challenges for academy-governed infrastructure for “open social scholarship.” Themes identified include technical fatigue and burnout in the current multi-platform environment, sustainability, and desires to reach and engage the right academic and non-academic audiences when appropriate.
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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 | 0.023 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.028 | 0.030 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".