“Poem is What?” Poetic Inquiry in Qualitative Social Science Research
Bibliographic record
Abstract
This paper articulates the findings of a two-year Canadian federally-funded postdoctoral study — Poetic inquiry in qualitative research: A critical survey — on the use of poetry in social science qualitative research practices. Based on a 1000+ page annotated bibliography gathered into a critical anthology as the data for this project, the discoveries emerged that are expressed below. The bibliography consists solely of poems included in over 230 studies found for this multidisciplinary project, supported by abstracts and brief contextual notes. Selection criteria for included studies were peer-reviewed journal contributions only, bracketing out anything that had appeared in book form, in ‘Poet's Corners’, and also excluding poems appearing in theses or dissertations. Some of these excluded poems are cited in a separate Appendix. These criteria were made simply to limit the scope of the study to a manageable scale. Most of this poetically-informed scholarship has appeared in the past decade, although some entries date as far back as the 1970's and 80's. Conclusions are that poetic inquiry most often addresses topics with clear affective dimensions, and can be distinguished between participant-based and self-study foci, with occasional examples of theoretical studies. Participant-based studies generally draw on the literary tradition of found poetry to represent participant data. Self-studies may address more philosophical, phenomenological and/or poststucturalist opportunities that present themselves through the use of poetry in social science contexts.
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 | 0.199 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.023 | 0.053 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".