Bibliographic record
Abstract
The work of Michael Heller on the so-called anticommons, or using the more recent moniker, “gridlock”, and the abundance of scholarship that it has generated, is one of the more significant recent concepts that has emerged from American property scholarship. Yet, in my view, Heller’s anticommons rests on a flawed view of private property ownership; indeed, some of his examples of gridlock have little to do with private property as such. Yet, private property is not nearly as absolute as the tragedy of the anti-commons claims or indeed requires. This flawed view, which posits private property as being more or less absolute, then benefits from the counter-balance or corrective provided by the recognition of an anticommons. Once property is seen in its proper light, the superstructure of the anticommons becomes unnecessary at best, obfuscating at worst. Starting with a more balanced view of private property, the central insight of the anticommons literature is already contained in the concept of private property. And the practice of private property has long tried to address the very different and complex challenges of fragmented and adjacent ownership that the anticommons perhaps oversimplifies. Thus at the very least, simplicity of thought demands that we not create unnecessary conceptual structures.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.051 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".