A Dynamic Process Model for Finding Informants and Gaining Access in Qualitative Research
Bibliographic record
Abstract
This article surfaces some of the emotional encounters that may be experienced while trying to gain access and secure informants in qualitative research. Using the children’s game of hopscotch as a metaphor, we develop a dynamic, nonlinear process model of gaining access yielding four elements: study formulation with plans to move forward, identifying potential informants, contacting informants, and interacting with informants during data collection. Underlying each element of the process is the potential for researchers to re-strategize their approach or exit the study. Autobiographical stories about gaining access for our PhD dissertation research are used to flesh out each element of the process, including the challenges we experienced with each element and how we addressed them. We conclude by acknowledging limitations to our study and suggest future and continued areas of research.
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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 | 0.067 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".