Bibliographic record
Abstract
The value of collaborative international research in addressing global public health challenges is increasingly recognized. However, little has been written about lessons learned regarding fieldwork to help guide future collaborative efforts. Through a research partnership between two Northern universities, one Southern university, and a Southern faith-based organization, we evaluated a school-based HIV prevention intervention with South African adolescents. In this article, we highlight the seven key fieldwork-related challenges experienced and identify the lessons learned. The underlying theme is that of reconciling a structured and reasoned "desk" planning process with the more fluid and unpredictable reality of conducting fieldwork. This concern is particularly significant in resource-deprived environments and/or contexts that are less familiar to Northern partners. Fieldwork is unpredictable, but obstacles can be minimized through meaningful participation in both planning and field research. Sharing practical lessons from the field can prove a useful resource for both researchers and practitioners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.125 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.010 | 0.014 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.051 | 0.015 |
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".