Indigenous Gendered Spaces: An Examination of Kenya
Bibliographic record
Abstract
Look at this shamba (farm). I had coffee trees from the top of the hill to the river. Every year the coffee co-operative told us the same story. There is no market for your coffee. The competition is high and prices are low. We could not uproot the trees for crop rotation. The government agents told us growing coffee was the way to progress. We were not allowed to plant maize or beans in between the coffee trees. After many years of no money and no food, we decided to cut down all the coffee trees and leave a few for our use… The women were the first ones to cut the coffee trees. Somehow everybody in our community followed our example…it is like we knew we had to do something to save ourselves and also the soil. Many women and men got sick from the pesticide sprays and those fertilizers we had to buy from the coffee board…I guess we had to do what we thought was best for our community (Muthoni, 1998, as told to Wane).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".