Evolution of an Open Source Strategy
Bibliographic record
Abstract
On June 8th, 2005, we officially launched the ePresence (http://epresence.tv/) Interactive Media Open Source Consortium, at the Knowledge Media Design Institute (KMDI), University of Toronto (UofT). We had been researching and developing ePresence, our webcasting, webconferencing, and archiving software project for about five years. Throughout the early phase of the project we used the system to produce live webcasts of KMDI's annual lecture series. Eventually word spread about our webcasting system and other universities, such as Memorial University in Newfoundland, became interested. It was obvious that the time to share our project with the world had come, but what wasn't obvious to us at the time was how we were going to do that.
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 | 0.080 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".