Bibliographic record
Abstract
When conducting research in an international setting, in a country different than that of the researcher, unpredictable circumstances can arise. A study conducted by a novice North American researcher with a vulnerable population in northern Ghana highlights these happenings with an emphasis placed on the ethical challenges encountered. An illustration from the research is used to highlight an ethical dilemma while in the field, and how utilizing a moral decision-making framework can assist in making choices about a participant's right to autonomy, privacy, and confidentiality during the research process. Moral frameworks, however, can never be enough to solve a dilemma since guidelines only describe what to aim for and not how to interpret or use them. Researchers must therefore strive to move beyond these frameworks to employ practical wisdom or phronesis so to combine the right thing to do with the skill required to figure out what the right choice is. The skill of practical wisdom must be acquired because without it international researchers indecisively fumble around with good intentions, often leaving a situation in worse shape than they found it.
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 | 0.304 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.027 | 0.219 |
| Scholarly communication | 0.049 | 0.046 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.019 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".