Creating a Canadian Library Publishing Community to Support Scholar-led Publishing
Bibliographic record
Abstract
Canada has a strong tradition of scholar-led publishing, with the majority of Canadian scholarly journals publishing independently of commercial publishers. This is made possible via a varied support network such as Canadian library publishers and Coalition Publica (as national, open infrastructure for digital scholarly publishing). Library publishers from over 40 academic libraries provide publishing or hosting services to more than 800 scholar- and student-led publications. However, until recently, practitioners in this space have had no formal organization to engage with their issues or to represent them to the wider Canadian scholarly communications community. This poster will outline the development of a Community Engagement Team to support Canadian library publishing. In the fall of 2023, a working group initiated by Canadian members of the Library Publishing Coalition (LPC) recommended a multifaceted approach to supporting the Canadian library publishing community after consultation with scholarly communications stakeholders. Initiatives recommended by the working group included striking a formal partnership between LPC and the Canadian Research Knowledge Network and the establishment of a library publishing Community Engagement Team in collaboration with the Canadian Association of Research Libraries. Coalition Publica, through its main partners Érudit and the Public Knowledge Project, is supporting the Canadian scholar-led journal community by developing and coordinating an open and sustainable national infrastructure for digital scholarly publishing. Library publishers are an important part of this national infrastructure, and Coalition Publica is an active participant in the new Community Engagement Team. This poster will illustrate how Coalition Publica and the Canadian Community Engagement Team collaborate in organizing a national network of library publishers and accelerate community-driven publishing in a national, bilingual context.
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 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.000 |
| Scholarly communication | 0.180 | 0.028 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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; both teacher heads agree on what is shown here.
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".