Social Sciences and Humanities Research and the Public Good: A Synthesis of Presentations and Discussions
Bibliographic record
Abstract
In May 2010, with the support of funds from the Social Sciences and Humanities Research Council (SSHRC) of Canada, a one-day workshop, entitled, “Social Sciences and Humanities Research as a Public Good: Identifying Research Prospects for Advancing Research Among Academic and Non-Academic Discourse Communities” was held in Montreal, Québec. The workshop brought together Canadian stakeholders involved in extending the reach of research (for the public good), including those involved in open access and knowledge mobilization, as well as organizations linked to the research community, and non-academic organizations with a clear mandate to include research in their activities or to extend the reach of research. This article presents a summary of the workshop presentations and a synthesis of the workshop discussions. The article also provides a discussion of the emergent issues arising from the workshop (such as the sustainability of open access journal publishing, the challenges of knowledge mobilization, and the limited media uptake of social sciences and humanities research), areas of inquiry that these issues open up (engaged scholarship and the engaged university, faculty reward structures, and public knowledge/knowledge mobilization as areas of scholarly inquiry), and collaborative next steps for stakeholders to take, to address concerns raised and to seize opportunities to advance shared interests.
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 | 0.042 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".