Shelves and Bins: Varieties of Qualitative Sociology in Rural Studies*
Bibliographic record
Abstract
It did not take us long to discover that the “field” of qualitative research is far from a unified set of principles promulgated by networked groups of scholars. In fact, we have discovered that the field of qualitative research is defined primarily by a series of essential tensions, contradictions, and hesitations. These tensions work back and forth among competing definitions and conceptions of the field. Further, these tensions exist in a less‐than‐unified arena. We discovered that the issues and concerns of qualitative researchers in nursing are decidedly different from those of researchers in cultural anthropology. Symbolic interactionist sociologists deal with questions that are different from those of interest to critical theorists in educational research. Nor do the disciplinary networks of qualitative researchers necessarily cross each other, speak to each other, or read each other. (Denzin and Lincoln 1994:ix It is an interesting time to be leaning over the fences of American farms. There are discussions, even arguments, in the land about whether farmers ought to change the way they farm …There have been arguments like this heard before …[T]he basic question about farming splits into many smaller ones. The answers multiply and become contradictory. Hence this effort to sort the questions onto different shelves, the answers into different bins …There is only one new idea developed here: there are really no new ideas in arguments about agriculture. (Wojcik 1989:ix, x, xii)
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 | 0.104 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.016 | 0.084 |
| Scholarly communication | 0.023 | 0.028 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".