Introduction to Equity and Digital Literacies: Access, Ethics, and Engagements
Bibliographic record
Abstract
This special issue arises out of a unique, jointly-sponsored conference held at the University of British Columbia Faculty of Education in the spring of 2016.The Equity in Digital Literacies conference brought together international scholars, Canadian scholars, and local community educators to address issues of (in)equities of digital literacies in homes, schools, and communities locally and across the world.As researchers at UBC and Simon Fraser University, we wanted to identify ways that educators are and should be responsive to the various constraints as well as the possibilities for engaging people in meaningful digital language and literacy practices.Many educational researchers who have focused on digital language and literacy practices perceive them to be more democratic (Dobson & Willinsky, 2009) and to foster more participatory, collaborative, and distributed engagements (e.g., Ito et al., 2009;Jenkins, Clinton, Purushotma, Robison, & Weigel, 2009;Knobel & Lankshear, 2006;Rowsell & Pahl, 2007).However, given that the phenomenon of digital practices occurs in broader social, political, and economic contexts, a more critical model of digital literacy (Warschauer, 2009) allows us to examine both the modes of inclusion and exclusion shaped by digital technologies.As advanced capitalist countries transition to a postindustrial capitalism where material production is replaced by information processing and automation, being able to use technology has become "the critical factor in generating and accessing wealth, power, and knowledge" (Castells, 2010, p. 93).At the same time, as the fulcrum of capitalist society in the knowledge economy, technology can lead to "one of the most damaging forms of exclusion" (Castells, 2010, p. 3).When digitization is set against this wider backdrop of society, tensions and imbalances of power inevitably surface.How one is able to access and use technology is not only dependent on one's possession of economic capital, but also contingent on government, institutions, and policies that enable such access among individuals and groups.Inequities arise from these conditions of access, and from differences in digital usage and tastes (North, Snyder, & Bulfin, 2008;Selwyn, 2017;Snyder & Prinsloo, 2007), valued differently across contexts, and resulting in divergent social and educational outcomes (Warschauer & Matuchniak, 2010).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".