Understanding exclusionary mechanisms at the individual level: a theoretical proposal
Bibliographic record
Abstract
On the basis of the social exclusion framework put forth by the Social Exclusion Knowledge Network (SEKN), we propose a framework that conceives social exclusion as a mechanism that limits access to rights, resources and capabilities needed for a healthy life. While it is widely accepted that drivers of social exclusion are structural, the consequences are experienced by individuals in their everyday lives. This article proposes an adaptation of the SEKN framework, illustrating additional basic elements that should be considered in the study of exclusionary mechanisms. We argue that studying access to rights, resources and capabilities is one way to capture the relational aspect of exclusion mechanisms. In doing so, we shift the focus away from the individual and direct the analysis towards contextual conditions that cause the emergence of certain individual attributes. We use the example of food insecurity experienced by individuals to illustrate how a specific problem can be the manifestation of different structural exclusion mechanisms that limit access to the rights, resources and capabilities required for a healthy life.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".