Bibliographic record
Abstract
Kenya‟s cut-flower industry has been praised as an economic success as it has provided jobs, income and infrastructure for the citizens of the country. Conversely, media coverage has criticized cut-flower production for causing negative environmental and social impacts. Cut-flower production in Kenya is concentrated on the southern shores of Lake Naivasha. The pressures of extraction are causing the naturally sensitive and variable lake to experience lake levels much lower than what the models had predicted. The cut-flower industry on Lake Naivasha has also been socially criticized for the poor working wages, the poor working conditions and the impacts of the increased population its employment attracts on the community of Naivasha. This paper looks into the economic, ecological and social implications of this industry, and assesses whether or not it is sustainable. The cut-flower industry‟s economic success is completely dependent on European markets, is not equitably distributed, and is vulnerable to business migration. Lake Naivasha is naturally sensitive to climatic changes and experiences much lake level variability. The increased unmeasured water extractions, habitat degradation and lack of adequate management exacerbates this situation. The civilians of Naivasha are suffering from a lack of infrastructure and security due to the population influx accompanying the cut-flower industry, without adequate wages or working conditions to access basic livelihood assets and high quality of life. For these economic, environmental and social reasons, it is determined that Kenya‟s cut-flower industry is not sustainable. Recommendations for mitigation of these impacts are given.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".