Bibliographic record
Abstract
Abstract Fieldwork involves long‐term research in which the investigator spends an intensive amount of time inquiring into the way of life of a specific human population. Used properly, it can result in extremely useful portraits of little‐known, misunderstood, or fully unknown ways of life. The main goal is to learn an inside point of view, or what it is like to live as a member of a specific sociocultural formation. Instead of relying on the statistical methods of quantitative research, fieldworkers gather information through a wide variety of ethnographic or qualitative methods. The reliability issue is dealt with through comparing information gathered through more than one research method and by analyzing information in relation to the broader context (e.g., economic, political, religious, institutional). Reliability may also be checked more formally through the use of simple counting schedules. Key methods include participant observation, several kinds of interviewing techniques, and self‐reporting methods. The researcher is always the first and primary research instrument of fieldwork. The key to fieldwork lies in the relationship of mutual trust established between the researcher and those with whom s/he is working. Without such a relationship, productive fieldwork is not possible.
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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.245 | 0.083 |
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".