SFU Knowledge Mobilization Hub Year Two Highlights
Bibliographic record
Abstract
SFU’s Knowledge Mobilization Hub aims to grow a culture of knowledge mobilization (KM) at SFU and contribute to SFU being recognized as a world leader in KM. Year two of the SFU KM Hub has been all about inspiring action and building capacity for KM at SFU through collaborations, instruction, and pre and post award supports. This two-page report provides a snapshot of the key achievements and milestones from October 2020 to September 2021. It outlines five initiatives: the launch of the Knowledge Mobilizers story series; SFU's membership in Research Impact Canada; the start of the SFU Research Impact Work Group; the Research Meets Policy @ SFU summer institute; and finally, a SSHRC grant awarded to the KM Hub. As in Year One, this report highlights three core areas of focus: consultations for KM support & navigation; capacity building training & events; and recognition efforts to highlight SFU mobilizers. The report also documents the number of stakeholders engaged, participants who attended KM workshops, and the amount of funding awarded to proposals supported by the KM Hub over the past year.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".