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Record W2164716375

Challenges for catchment management agencies: lessons from bureaucracies, business and resource management

2000· article· en· W2164716375 on OpenAlexaff
Karyne M. Rogers, Dirk J. Roux, Harry Biggs

Bibliographic record

VenueWater SA · 2000
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsKruger (Canada)
Fundersnot available
KeywordsEcosystem managementAdaptive managementBureaucracyNatural resource managementNatural resourcePopulationResource management (computing)BusinessEnvironmental resource managementManagement by objectivesHuman resource managementEconomicsKnowledge managementPolitical scienceComputer scienceSociologyEcologyEcosystemMarketing
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.078
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0150.026
Scholarly communication0.0200.024
Open science0.0030.010
Research integrity0.0140.020
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.086
GPT teacher head0.259
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations88
Published2000
Admission routes1
Has abstractyes

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