Literacies of Civic Engagement: Negotiating Digital, Political and Linguistic Tensions
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,009 | 0,020 |
| Communication savante | 0,026 | 0,015 |
| Science ouverte | 0,002 | 0,015 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,019 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».