Feeling clumsy and curious. A collective reflection on experimenting with poetry as an unconventional method
Bibliographic record
Abstract
Abstract In this paper, we offer a collective, multi‐vocal reflection on using poetry for research purposes. These were reflections on an online sub‐plenary session organized as a workshop, which was held at the European Group for Organization Studies conference in 2021. During this workshop, the first three authors presented a step‐by‐step method for doing poetic inquiry and invited participants to apply it to their own empirical data or research praxis. The method was created in response to the marginalization of affect and embodiment in mainstream research in organization studies. Poetic inquiry aims to formulate specific practices of “writing differently” that assist researchers in their attempts to analyze and articulate their findings in embodied and affective ways. In this paper, we describe the method and bring together multi‐vocal reflections from the participants and organizers of the workshop on the affects of poetic inquiry and the (ethical) questions that it poses.
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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 | 0.043 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".