How Institutional Cultures Affect Results: Comparing Two Old-Growth Forest Mapping Projects
Bibliographic record
Abstract
This paper explores the institutional and geographic factors that affected the outcome of two old-growth forest mapping efforts undertaken in 1989–1990 in the Pacific Northwest region of the United States. The projects mapped old-growth forest stands within US National Forests that support the endangered northern spotted owl. The projects, conducted by the US Forest Service (the land manager) and the Wilderness Society (a conservation organization), obtained old-growth acreage totals that differed by a factor of two. This difference was largely attributable to the organizational context of each project. Both were constrained by short time lines imposed by the US Congress and by impending litigation. Motivations for the two organizations, however, were very different: Congress compelled the Forest Service to do the mapping, whereas the Wilderness Society used the geographic information for conservation advocacy. The two organizations also varied by the level of financial resources allocated and the methods (remote sensing versus aerial photo interpretation) employed. In my comparison of the two projects, I examined the events leading up to the spotted owl controversy and investigated the nature of the institutions involved. To understand the methods of the projects, I obtained the published literature resulting from the two projects and interviewed the principals of each project. I then obtained the data sets, put them into a common format, performed a spatial overlay, and compared the results using confusion matrices and visual analysis. When the two data sets were compared directly, there was little pattern evident in the differences. This lack of pattern made it difficult to draw any conclusions about the relative accuracy of the studies. It is inappropriate to infer that the results of either project were better; however, it is critical to understand the causes of the disparate results. The research found that merely by providing an alternate set of maps of old growth, the Wilderness Society "won" by casting doubt on the maps produced by the Forest Service. I also identified several institutional factors that affected the projects' outputs, namely budget, technology, staffing, study area, and institutional agendas and requirements. It is hoped that an understanding of these factors and disparate project results will help users of the two data sets understand their inherent biases and appropriate usage.
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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 | 0.048 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".