Bibliographic record
Abstract
Resilience is a concept employed within an increasing scope including ecology, security, social sciences and human psychology. It is applied to various social groups including disabled people. At the same time there are numerous critiques within and outside of the disability community of how resilience is conceptualized and operationalized. The media is a tool used to inform the public and to shape public discourses. Many discourses are seen to be in need of governance actions such as science and technology or global health including the resilience discourse. Our study contributes to the discussions around resilience and disabled people in two ways. First we add qualitative data on the narrative of resilience in relation to disabled people in Canadian newspapers. Our findings reveal very little mentioning of disabled people within the newspapers covered whereby the nature of the coverage exhibits many of the facets for which the resilience discourse is critiqued within and outside of the disability community. Secondly we use a governance lens to analyze the existing governance of resilience discourse to ascertain whether a governance of resilience discourse might be a place for the disability community to shape resilience discourses. We suggest that the existing governance of resilience discourse has to change substantially, to be able to govern resilience discourses in a way that prevents negative impacts of resilience discourses on disabled people. Given the premise of governance as used in other discourses disabled people could lead to a positive change by influencing the resilience governance discourse.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.006 |
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".