Being Alive and at Risk: Building a Meshwork of Risk and Health Among Young Adults
Bibliographic record
Abstract
The presented dissertation employs Tim Ingold’s conceptualization of meshwork in combination with narrative methodologies to theorize and describe the implications of a narrative meshwork approach in the context of risk-related research. Because of the many broad and divergent definitions of risk as well as its all-encompassing and continuously evolving nature, risk scholarship has typically suffered from an inability to unpack the interwoven and interconnected nature of risk (and life) itself. In response to this reality, the dissertation that follows seeks to engage in the development of a critical risk theory that exemplifies nimbleness in theoretical approach and application. More specifically, the purpose of this research is to theorize and explore meshwork of risk as a significant new avenue to contextualizing risk scholarship within diverse health settings. Step one of the dissertation (presented in Chapter 2: Meshwork of Risk: A Theoretical Argument) employs a thorough review of Ingold’s conceptualization of meshwork in combination with a narrative case study concerning American sprinter Sha’Carri Richardson in an attempt to theorize meshwork of risk while exploring storied narratives of gender and race. Following the theoretical exploration, the dissertation applies narrative interviews in combination with auto-photography to examine experiences relating to the negotiation of meshwork of risk within young adult sport and leisure environments (Chapter Three: A Narrative Typology of Risk). Because of the unique risky environment offered by sport and leisure, engaging with young adults in this manner provides vital perspective to understanding what it means to operate, live, and grow within a risk meshwork. Specifically, the analysis presents a narrative typology of risk that works to identify three key narrative types among the young adult population: (1) the responsibility narrative; (2) the pursuit narrative; and (3) the grand risk narrative. Finally, in Chapter Four (Implications and Conclusions), I practically apply this typology to demonstrate its applicability across physical cultural studies, public health, and health communication; specifically, the so-called ‘concussion crisis’, which represents a fascinating avenue for exploring the dynamics of risk within the education and prevention processes, is used as an explorative case.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".