Distinct Yet Synergetic Contributors to Mixed Methods Research: Intersections for MMIRA and <i>JMMR</i>
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
The formalization of the synergistic relationship between the Journal of Mixed Methods Research (JMMR) as an affiliate of the Mixed Methods International Research Association (MMIRA; http://mmira.wildapricot.org/) presents an opportune moment to highlight the past, present, and future intersections for these influential yet distinct contributors to mixed methods research (MMR). JMMR formally becoming an affiliate of MMIRA represents a natural progression in the synergistic relationship that has been enjoyed since the establishment of MMIRA in 2013. From the founding of MMIRA, an important member benefit has been online access to JMMR. In addition to the “In This Issue” section that highlights the contributions of this issue’s articles, in this editorial, we describe the affiliate relationship between MMIRA and JMMR as an illustrative example of a sustained “synergy” promoting mutually advantageous compatibility from distinct efforts. Specifically, we present past, present, and future intersections between JMMR and MMIRA in the description of MMIRA’s history, accomplishments, and strategic plan and then suggest future directions for synergies between JMMR and MMIRA.
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 | 0.319 | 0.228 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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