How Institutional Cultures Affect Results: Comparing Two Old-Growth Forest Mapping Projects
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».