Challenges for catchment management agencies: lessons from bureaucracies, business and resource management
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
Catchment management agencies (CMAs) have no tested precedent in South Africa and will have to evolve in complex and changing business, social and natural environments as they strive to ensure that equity and social justice are achieved within ecologica l limits. Traditionally, very different styles of management have been used for resource exploitation and resource protection and this wi ll present a serious dilemma for CMAs. As the human population has grown and natural resources have declined, there has been increased effort to control nature in order to harvest its products and reduce its threats. Initially such “command-and-control” management has been successful as agencies prosper on short-term gains. However, when natural variation is reduced the ecosystem loses its resilience and ability to “bounce back” from disturbances. The first lesson we can learn is that the longer term consequence of command-and-control management is always either a reduction or cessation of resource supply. The second lesson comes from adaptive resource management (ARM). ARM acknowledges that, because nature is in a continual state of flux and our understanding of ecosystem functioning is poor, a fundamental problem for decision makers is that they mu st deal with uncertainty from an imperfect knowledge base. A learning-by-doing approach becomes a prerequisite for effective management. Unfortunately, there has been a tendency to superimpose adaptive management on bureaucratic institutional structures. Such flouting of the fundamental management axiom “form must follow function”, has thwarted many attempts at adaptive management. This provides our third lesson. Recognition that authoritarian, command-and-control, bureaucracies respond too slowly to survive in changing environments has led managers in government, industry and businesses to create “learning institutions” which combine adaptive operations and generative leadership (lesson four). Effective knowledge management is seen as a critical success factor in turning command-and control management into adaptive, learn-by-doing management (lesson five). CMAs which recognise the dangers of excessive command and control, the need to integrate stakeholder values and activities, and the potential of an adaptive and generative management approach, will need to structure their activities carefully. At present there is much focus on the structure of CMAs and much less on how they should function. Form is preceding functio n in many instances. When function is discussed it centres on how regulatory mechanisms and permit systems will keep resource use under control. The concern is seldom with how the ecosystem will be managed. This sort of thinking could lead to a classic command-and-control management approach if not tempered with a more adaptive process. Strategic adaptive management (SAM) is a local derivative of ARM designed to generate consensus management which is inclusive, strategic, adaptive and creative. SAM is a process in which effective knowledge management is central to building a partnership between science, management and society to achieve a common vision. It has considerable potential for application t o CMAs.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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 découle