What Matters for the Job Performance of Field Advisors: A Case of Participatory Forest Management in Madhupur Sal Forest in Bangladesh
Bibliographic record
Abstract
This study analyzed the determinants of the job performance of field advisors who were working in a remote forest area. A stakeholder analysis was conducted to identify advisory organizations working in the Madhupur Sal forest, Tangail, Bangladesh. Data from 87 field advisors were collected in face-to-face interviews. Binary logistic regression analysis was performed to identify the factors affecting the performance of the field advisors. Various factors were identified at the organizational and the individual levels. Important organizational-level variables were coordinated with other organizations, existence of economic incentives for fieldwork, presence of a monitoring and evaluation system, total number of staff, staff training, presence of a system of rewards and punishments, existence of travel allowance, and access to computer and internet facilities. Variables at the individual level included the interaction with forest researchers and the use of a group approach for information sharing as significant determinants of satisfactory performance of field advisors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".