Inadvertent construction of inequality by digital media: Conceptualizations via the international classification of functioning
Bibliographic record
Abstract
This paper posits that the concept of ability acts as a confounder in digital media discourse, potentially contributing to persistence of classism and economic inequalities to the detriment of social outcomes. The International Classification of Functioning (ICF) is used as a theoretical framework, with the objective of interrogating the discursive concept of ability in digital media discourse and coding of class. Social structures and other environmental structures emerge as critical to impair ability, although historically media discourse – including scientific literature – have emphasized personal factors including existing marginalizations such as race, ethnicity, or gender. Marginalization arising from existing social structures and other environmental structures, therefore, interacts with digital media to construct ideologies surrounding class in relation to the symbolic annihilation of other forms of intersectional marginalization. The concept of structural polarization is proposed via assessment of a British framework on media stereotypes, applied to demonstrate how media discourse around marginalization can result in social stratification regardless of intent. With tremendous positive potential for digital media to be applied in social work advocacy, community empowerment, and direct provision of essential human services, twelve considerations are organized into a preliminary conceptual framework – toward supporting future media in minimizing outcomes associated with inadvertent disempowerment.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".