Individual stakes and collective ideology in tension
Bibliographic record
Abstract
This paper presents a critical hermeneutic interpretation of the meanings, practices and values associated with physical and spatial obstacles present in the shopping experiences of individuals with mobility or visual impairments. The social model of disability, which positions disablement in societal attitudes, understandings, practices, and institutions, has reinforced a view that built environments tend to limit, restrict, segregate, and even oppress differently-abled individuals. Despite the pervasiveness of this view, little research has empirically explored the experiences of, responses to, or evaluations of environmental barriers. In the current study, we interviewed and observed four individuals with visual impairments and four individuals with mobility impairments in hopes of better understanding these topics within a shopping context. Reconstructing participants’ discourses into their implicit narrative structures, we found that participants generally re-established equilibrium in their emplotted encounters with obstacles in the mall, transfiguring challenging and dysfunctional environments into coherent and functional spaces. Our findings challenge the notion that the constructions, meanings, and values of physical and spatial obstacles are universal or intrinsic, and point to the agency of participants in shaping their own plots. We suggest that future research ought to examine physical and spatial obstacles within even broader frameworks of meaning.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.051 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".