Ensuring Quality While Expanding Quantity: How to effectively scale your education program
Bibliographic record
Abstract
Over the past ten years, Wiki Education has more than tripled the number of courses participating in the Wikipedia Education Program in the United States and Canada at the postsecondary level. At present, the Wiki Education team provides support for more than 700 courses and roughly 10,000 students each academic year. In this workshop, we’ll provide attendees with step-by-step guidance on how to conduct outreach to new program participants, brainstorm strategies for engaging program participants to promote retention, and demonstrate how to provide quality support while significantly scaling your program. We’ll introduce many of the tools Wiki Education has used to rapidly grow over the past ten years such as databases to track participants and our course dashboard to track Wikipedia work. We’ll specifically seek to ascertain the specific educational and cultural contexts of workshop attendees to better understand and formulate successful strategies for scaling. We’ll explore what barriers exist to scaling efforts and how these might be overcome. The workshop will also provide guidance on how to scale while considering critical issues around representation and access for a variety of communities.
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 | 0.108 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.020 | 0.035 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".