Bibliographic record
Abstract
Integration—or the meaningful bringing together of different data sets, sampling strategies, research designs, analytic procedures, inferences, or the like—is considered by many to be the hallmark characteristic of mixed methods research. Poetry, with its innate capacity for leveraging human creativity, and like arts-based research more generally, which can provide holistic and complexity-based perspectives through various approaches to data collection, analysis, and representation, can offer something of interest to dialogue on integration in mixed methods research. Therefore, in this editorial, we discuss and promote the use of poetry in mixed methods research. We contend that the complexities and mean-making parallelisms between poetry and mixed methods research render them relevant partners in a quest to complete the hermeneutic circle whose origin represents experiences, phenomena, information, and/or the like. We advance the notion that including poetic representation facilitates the mixed methods research process as a dynamic, iterative, interactive, synergistic, integrative, holistic, embodied, creative, artistic, and transformational meaning-making process that opens up a new epistemological, theoretical, and methodological space. We refer to this as the fourth space, where the quantitative, qualitative, mixed methods, and poetic research traditions intersect to enable different and deeper levels of meaning making to occur. We end our editorial with a poetic representation driven by a word count analysis of our editorial and that synthesizes our thoughts regarding the intersection of poetry and mixed methods research within this fourth space—a representation that we have entitled, “Dear Article.”
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 | 0.197 | 0.323 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.028 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.015 |
| 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".