Unveiling the nexus between maltreatment of smallholder youth farmers and agricultural productivity in Tanzania
Bibliographic record
Abstract
Despite global and developing countries' efforts to address maltreatment across various sectors, limited attention has been given to its impact on agriculture. This study investigates the effects of maltreatment of smallholder youth farmers specifically physical abuse, sexual abuse, and emotional abuse on agricultural productivity among smallholder youth farmers. Employing a statistical research design, data from the Tanzania Integrated Labor Force Survey 2020/21 are analyzed. The study utilizes a Multivariate Probit (MVP) model to estimate determinants of maltreatment, and instrumental variable models (Two Stage Least Squares, Two Stage Residual Inclusion, and Control Function Approach) to estimate the effects of maltreatment on agricultural productivity with proximity to local law enforcement as an instrument to control endogeneity. The results reveal that Tanga (21.46%), Morogoro (17.08%), Kilimanjaro (17.06%), and Dodoma (15.00%) exhibit a high prevalence of maltreatment practices among youths, whereas Geita, Kusini Pemba, Kusini Unguja, Mjini Magharibi, Njombe, Rukwa, Simiyu, and Tabora display relatively few instances. Furthermore, factors such as gender, age, residence, and disabilities are key determinants of maltreatment. Additionally, maltreatment has varying effects in reducing agricultural productivity significantly such that physical abuse (β = -0.2315, p<0.01), sexual abuse (β = -0.4281, p<0.01), and emotional abuse (β = -0.1965, p<0.01). This study implies that addressing maltreatment is crucial for enhancing the well-being and productivity of smallholder youth farmers. Moreover, it informs policy on the need for targeted interventions to mitigate maltreatment and recommends gender-sensitive agricultural policies, rural development initiatives, educational and skill-building programs, disability-inclusive policies, workplace support, and mental health resources, and the integration of technology for sustainable agricultural practices.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".