Bibliographic record
Abstract
Earlier this year, SPARC launched Connect OER—a platform to share and discover information about Open Educational Resources (OER) activities at campuses across North America. Through Connect OER, academic libraries create and manage profiles about their institution’s efforts on OER, producing valuable data that we use to populate a searchable directory and produce an annual report. As the first Connect OER Annual Report, this document summarizes insights from the Connect OER pilot, which ran from May - July 2017. The data encompass 65 SPARC member libraries spanning 31 U.S. states and five Canadian provinces who participated in the pilot. Our analysis provides a snapshot of what is happening on the ground level with OER at this subset of institutions. Our intent is that these insights will help inform SPARC members, the open education community, and the library community at large about current trends, best practices, and the collective impact being achieved through OER at participating institutions. The purpose of the Connect OER pilot was to identify: • Which SPARC member campuses were actively engaging in OER • What role the library—in relation to other campus stakeholders—was playing in these efforts • What institutional resources have been made available to support these efforts • What kinds of activities and partnerships exist on campus • What the overall impact of OER has been at SPARC member campuses As the project transitions out of the pilot, we hope that the Connect OER directory will continue to grow, so that future annual reports can provide insights based on a broader range of institutions across North America. We recognize that the directory will never reach a state of completion since activities are always evolving, but our annual snapshots will provide a meaningful illustration of the reach of the OER movement, and the impact it has for students. KEY INSIGHTS 1. Libraries are the most engaged entity on campus in efforts to advance OER 2. Within libraries, the department most actively engaged in advancing OER is Scholarly Communications 3. Science is the academic field with the most OER traction 4. Nearly half of the participating institutions have a faculty or staff person with explicit OER responsibilities 5. OER grant programs are the most common type of campus OER program reported 6. SPARC member institutions saved students an estimated $5 million through the use of OER in the 2016-2017 academic 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 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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.083 | 0.071 |
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".