Quali(a)tative Methods: Sense-Based Research in the Social Sciences and Humanities
Bibliographic record
Abstract
This paper begins by tracing the sensory turn in the human sciences—most notably, history and anthropology—which, in turn, gave rise to the interdisciplinary field of sensory studies. The latter field is articulated around the concept of the sensorium (defined as the entire sensory apparatus, including the extension of the senses via diverse media, as an operational complex) and the notion of qualia (defined as those aspects of the material world, such as color and sound, that are contingent on the human perceptual apparatus—in contrast to the inherent or elementary properties of materials, such as number or form, which are not). Sense-based research in the human sciences is tied to sensing and making sense together with others. Its methodology of choice is sensory ethnography, or “participant sensation.” This method departs from the emphasis on observation in conventional qualitative research, as well as the latter’s reliance on such verbocentric methods as the questionnaire or focus group. Sensory ethnography highlights the primacy of the quali(a)tative dimensions of our being together in society. It extrapolates on Georg Simmel’s point: “That we get involved in interactions at all depends on the fact that we have a sensory effect upon one another” (as cited in Howes 2013). In part II of this paper, a critique is presented of the diminution of the quali(a)tative in the context of the Scientific Revolution of the sixteenth and seventeenth century, the cognitive revolution of the mid-twentieth century, and the scientization of the senses in the Sensory Evaluation Research Laboratory. These revolutions are problematized for their lopsidedness: the privileging of the infrasensible over the sensible and elemental (or atomistic) over the phenomenal in the case of the Scientific Revolution; the neuronal over the sensual and social in the case of the cognitive revolution; and, the unimodal (or one-sensation- and one-sense-at-a-time) over the multimodal, as well as the reduction of “significance” to the statistical, in the case of the research protocols of the sensory science laboratory. The paper concludes by presenting the results of a series of case studies in sensory ethnography that push the bounds of sense by leading with the senses and bringing the quali(a)tative back in.
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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 | 0.148 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.008 | 0.077 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".