What Happens when Funders Misunderstand the Strengths of Qualitative Research Design
Bibliographic record
Abstract
Differences in what makes for a good quantitative or quali-tative research design often lead funders to misinformed evaluations of the strengths of exemplary qualitative research. Based on the author’s experience with numerous national funders in Canada and the US, problems getting qualitative research funded are discussed. Specifically, sampling issues will be looked at along a continuum, compar-ing monocular, homogenous sampling of marginalized voices more in keeping with positivist research principles familiar to funders to the polyocular, heterogenous innova-tion popular with qualitative researchers who seek multiple voices across multiple contexts. Successfully funded studies will be discussed as examples of how to convince funders to evaluate qualitative research on its own merits, as well as a number of unsuccessful grant applications that were evalu-ated with criteria that seemed paradigmatically incongruent with qualitative designs. Four strategies my colleagues and I have used will be highlighted. These strategies I call: dressing up; sleeping with the elephant; search but never find; and table scraps. The advantages specific to qualitative KEY WORDS: funding mixed methods research criteria review process
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 | 0.741 | 0.817 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.015 | 0.038 |
| Scholarly communication | 0.030 | 0.040 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".