Literacies of Civic Engagement: Negotiating Digital, Political and Linguistic Tensions
Bibliographic record
Abstract
Introducing the Special IssueLanguage and literacy practices are instruments of power and are inherently political.What languages we speak (and where we speak them), how we use literacy, and who we speak to, are issues that are intimately entwined with questions of belonging, identity, status, and citizenship.In the light of current events in Canada, and around the world, negotiating the language of belonging and citizenship are as contested as ever.What is more, ongoing changes in the ways that people make and consume texts remind us of the need to engage with such questions frequently and thoughtfully.Of course literacy refers to much more than just reading written texts.In her keynote address on Literacy and Civic Engagement, Jacqueline Jones Royster quoted Sojourner Truth who responded to a literacy prerequisite for voting rights with the telling, "You know, children, I don't read such small stuff as letters, I read men and nations" (as quoted in Royster, 2007, p. 4).Language, literacy, power, men, and nations are all being taken up in the contributions in this special issue.Thinking about these issues in the wake of the 2016 US presidential election, multiple examples of the ways in which language and literacy practices across texts and spheres hold important-and sometimes contradictory-meanings have arisen.Take the example of the multiple meanings given to terms like "fake news".Initially, the term "fake news" was used by mainstream media sources to describe content farms that hosted unsourced, unverifiable and fictitious news stories intended to elicit responses and be shared digitally (Marchi, 2012).Later, the term was used by US President Trump in particular, largely on Twitter, to refer to critical media coverage of his presidency, campaign, and the events leading up to it.Looking at the evolution of the term "fake news" and the power this term has been given reminds us that what we communicate, whom we communicate with, and how we are doing this communicating are inherently political.The spaces where we communicate from and our link to these places are also political-both in the spaces we inhabit and in the digital realm.For example, we are white settlers interested in issues of language, belonging, and civic engagement.I am writing this editorial from unceded Wolastoqiyik and Mi'kmaq territory-Fredericton, New Brunswick-and Diane writes from traditional unceded Algonquin territory in Ottawa, Ontario.What does it mean to write about issues of language, literacy and civic engagement from unceded lands?How might we think about unsettling these issues?The University of New Brunswick's Elder-in-Residence Imelda Perley, uses digital spaces, such as Twitter to share teachings of the Wolastoq language.On June 5, 2017, for example, Elder Perley tweeted, "Psiw Ntulnapemok-(pss-eow-ndole-nah-beh-mg) all my relations.A term used to honour all of creation, animal, earth, water, winged & tree clans."Using
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 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".