Building an Open Source Company One Penguin at a Time
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Roaring Penguin Software Inc. started as a one-person consulting company in 1999. A year later, David F. Skoll, the company's President and CTO, was asked to develop an e-mail filtering tool. David developed MIMEDefang, an e-mail filter that used Sendmail's Milter facilities. David donated the code to the open source community, and kept developing MIMEDefang as a free tool for system administrators. Today, the MIMEDefang code is available at at the website. By 2002, it was obvious that there was a need for a packaged mail-filtering solution suitable for end-users. David decided to go ahead and write what became CanIt-PRO. Whereas MIMEDefang is suited to system administrators who are familiar with Perl and comfortable with writing their filtering policies in Perl, the CanIt product line allows end-users to control their filtering through a simple web-based interface. The company transformed itself from a consulting company to a product development company. This transformation required significant changes.
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.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.024 | 0.021 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it