Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Differences in what makes for a good quantitative or qualitative 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, comparing monocular, homogenous sampling of marginalized voices more in keeping with positivist research principles familiar to funders to the polyocular, heterogenous innovation 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 evaluated 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 designs and sampling will be detailed in order to propose a template for funders to evaluate the merits of qualitative design.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it