Bibliographic record
Abstract
It has been assumed so far that all goods fall into one of two categories. Pure public goods are non-rivalrous in consumption, meaning that one person's consumption of any of these goods does not interfere with any other person's consumption of the same good. The clarity of your radio reception, for example, is independent of the number of other listeners. Private goods are rivalrous in consumption, meaning that only one person can consume each unit of these goods. Food and clothing are examples of goods in this category. But there are many other goods, including parks and recreational facilities, police and fire protection, and roads and bridges, that do not fit into either category. Consumption of one of these goods by another person reduces, but does not eliminate, the benefits that other people receive from their consumption of the same good. These goods are called impure public goods , and are said to be partially rivalrous or congestible . Impure public goods also differ from pure public goods in that they are often excludable. Access to many recreational facilities is controlled, and toll roads and toll bridges are not unfamiliar. Fire and police protection are more problematic. Controlling access to these services is more difficult, and even if it were feasible, it would raise serious ethical questions. The possibility of controlling access to impure public goods has two important implications. First, provision by private firms or by governments on a “fee for service” basis becomes possible, because free riding can be eliminated.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".