Rethinking the Fiscal Relationship Between Public Lands and Public Land Counties: County Payments 4.0
Bibliographic record
Abstract
In 1908, Congress authorized payments to local governments, including counties and school districts, to compensate for the non-taxable status of the newly established forest reserves within their boundaries. The original program shared revenue generated from commercial activities on public lands, e.g. timber harvesting, not anticipating the major changes in the volume and types of activities on National Forest lands, particularly in the Pacific Northwest, that have played out over the past century. Two subsequent reforms – the appropriated Payments in Lieu of Taxes (PILT) in 1976 and ‘transition’ payments made between 1990 and 2018, including payments associated with the Northwest Forest Plan and the Secure Rural Schools and Community Self-Determination Act (SRS) – have yet to deliver a permanent or effective policy solution that matches county payments to local governments’ economic needs or forest management objectives. This paper analyzes three policy options: a status quo option of PILT and revenue sharing payments; reauthorization of SRS; and the creation of a new permanent trust fund at the federal level. The paper concludes that the trust option (‘County Payments 4.0’) could resolve key challenges by stabilizing and growing revenue over time, eliminating the need for cycles of conditional appropriations, and providing flexibility to address economic and forest management needs in public land counties.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".