The Perils of Property Speak in Academia
Bibliographic record
Abstract
"The challenge that we face is not just about lawsuits and public policy. It???s also about a larger cultural pathology ??? the idea that knowledge and creative works should be owned outright an absolutely. I call this political and moral orientation Property Speak. It a belief that knowledge ought to be enclosed in tight little envelopes of property rights. The idea, of course, is that copyrights and patents reward people for their creative labors, encourages their work to be sold in the marketplace, and thereby generates wealth. What???s not to like? The premise is that knowledge cannot achieve its true value without being propertized. After all, if knowledge is free to share ??? if it has no price -- how could it possibly be valuable?"
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 | 0.029 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.061 |
| Scholarly communication | 0.031 | 0.029 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".