Who gets the human appropriation of net primary production?: Biomass distribution & the ‘sugar economy’ in the Tana Delta, Kenya
Bibliographic record
Abstract
In this article we focus on the connection between purchases of land and the emerging ‘biomass-economy’, analysing \nbiomass distribution in a region targeted for land-grabbing in order to understand the process from both bio-physical \nand political ecological perspectives. We narrow the focus down to a case study in the Tana Delta, Kenya, one of the \nnew commodity frontiers in the recent large-scale land acquisitions, employing an indicator derived from social \nmetabolism analysis — the Human Appropriation of Net Primary Production (HANPP). This allows us to examine \nbiomass flows in the Delta, combining a biophysical perspective with a political-ecology analysis of the interests, \nstakes and power politics in the delta. The first section introduces the conceptual tools and theoretical framework, \nexpanding on the concept of the ‘sugar economy’ as a socio-metabolic transition, and material and energy flow \nanalysis (MEFA) as valuable instruments in gauging sustainability and potential sites of conflict over biomass. The \nsecond section contextualises the case study of the Tana Delta in Kenya as a site of conflict over biological resources \nthrough an analysis of property rights and historical dynamics. The third section presents the results of the analysis of \nbiomass distribution. The fourth and fifth sections offer discussion of the results and the conclusions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".