Bibliographic record
Abstract
Qualitative field research can capture the life worlds and definitions of the situation of informants often not reported in quantitative studies. Post hoc reflections of how more seasoned researchers define, assess, and interpret the process of entering the field and the interview dynamic between the researcher’s subjectivity and the subjectivity of informants are widespread in the qualitative research literature. However, seldom are the personal stories and reflections of neophyte researchers voiced in published accounts. This article accounts for my experiences in researching the “dirty work” of frontline caseworkers and the importance of practicing empathy while managing a boundary. I emphasize the practical sense-making challenges of managing a delicate balance between under and over rapport in researching homeless shelter caseworkers as an occupational group. My experiences underscore the challenging dynamics of maintaining a professionally oriented research-role, as well as the crucial importance of boundary work and distancing as practical strategies to qualitative interviewing.
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 | 0.033 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.039 | 0.050 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.010 | 0.027 |
| Insufficient payload (model declined to judge) | 0.002 | 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".