Creating Connections: Library OER Services and Impact Advocacy
Bibliographic record
Abstract
This presentation was featured at the Open Education Conference 2025. The presentation explored embedded advocacy strategies within library-based Open Educational Resources (OER) services to advance and build connections to impact at a post-secondary research institution in Canada. With increased institutional support for initiatives in Open Science, research impact, and student success, there are natural connections for raising awareness about and highlighting OER impact for different contexts. Attendees will gain insights into embedded approaches for demonstrating the value of OER, including data collection and sharing strategies, identifying impact metric pathways, and fostering community. This session will include adaptable resources for attendees, highlighting of technologies used to support this work, and opportunity for knowledge sharing. Attendees of this session will be able to: (1) consider approaches to embed connections to impact within library-based OER services to reflect institutional initiatives such as Open Science, research impact, and student success; (2) reflect on advocacy activities for fostering community through OER initiatives highlighting OER impact; and (3) implement tools to support tracking and outreach for demonstrating the impact of OER at their institutions. A session recording is available on YouTube at https://youtu.be/Y6ONIlmxN-Q
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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.002 | 0.032 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.085 | 0.016 |
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".