Scaling Up Collaboration Online: Toward a Collaboratory for Research on Canadian Writing
Bibliographic record
Abstract
This article asks researchers of Canadian writing to reflect on collaboration as increasingly crucial to how we do our work in the context of digital environments that increasingly shape our work through their tools and resources. Scholars are in a position to help address major gaps in both online cultural content and digital infrastructure in Canada, both of which are vital to the continuing study of literature. Given the lack of a national digitization initiative and increasing government cuts, the need for high-quality Canadian web content and the interests of scholars in Canadian writing converge. The article describes the Canadian Writing Research Collaboratory as one attempt to addressing these gaps, while also outlining the substantial challenges—which are finally cultural rather than technical—associated with developing incentives to collaboration, fostering adherence to best practices, and demonstrating value in virtual research environments and the work that supports them. However, if emerging digital research infrastructures can foster collaboration, open access, and sustainability, they will make a historical difference to the cultural infrastructure and cultural memory of Canada.
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 | 0.083 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.095 | 0.040 |
| Scholarly communication | 0.039 | 0.020 |
| Open science | 0.006 | 0.049 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 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".