Richard Stallman on Software Patents 2005-05-18
Bibliographic record
Abstract
On 2005-05-18 Richard Stallman visited University of Calgary to talk about the dangers of software patents. Hosted by Calgary UNIX Users Group. ... RMS makes a few references specific to Canada, but most of his talk is applicable to any country. ... This footage was recorded in HD 1280x720x30p at 16x9. I have pan/scanned it to fill a 4:3 frame. For talking head footage, didn't see any point in uploading HD resolution footage. And IA isn't generating properly formatted derivatives of 16:9 (anamorphic) MPEG-2 footage yet. ... Audio was recorded onto an iRiver MP3 player/recorder (iFP-790) with an external passive microphone plugged in. There's a couple shots you can see it sitting on the table in front of RMS. Syncronized it with video in post. I find the iRiver a great way to capture audio, but an external microphone is needed as the iRiver tends to clip waveforms. The incoming volume control does NOT work for the built in mic, and the levels are too loud for any practical use. Darn shame. ... If anyone has need of the original HD footage, let me know. I can pull it off the camera and post small clips of the raw footage to IA if particular segments are good to anyone. The 16:9 might cut off less of RMS at times too (I was lazy when pan/scanning). Too darn big to post the whole HD thing. Segments with missing footage are exactly that. I was switching tapes or the STUPID JVC copy-from-camera-to-hard-drive program clipped my footage. If you need a missing piece, I'll see what I can do, or CUUG will probably have a 720x480 copy off someone else's camera. ... If you're listening to the MP3 or OGG ... the long silent pauses are RMS drinking Pepsi.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.484 | 0.424 |
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".