Young Quebecers in a situation of precarity and their digital literacy practices
Bibliographic record
Abstract
In its latest report based on PIAAC data, the Institut de la statistique du Québec (2015) notes that the young people aged 16 to 24 whose education got interrupted did not generally reach or exceed the level 3 in Problem solving in Technology-Rich Environments. Yet, young people in Western countries are often portrayed homogenously as digital native. This may be explained by the fact that studies have mainly focused on ‘Anglo-American or middle-class contexts’ (Prinsloo and Rowsell, 2012: 271). Using a New Literacy Studies perspective, this paper challenges narratives about young people’s digital literacy practices. It draws on data collected in 2012 in two community-based organisations for young people in Quebec (Canada). In total, 122 hours of participant observation were undertaken and 21 research interviews were conducted (14 young people and 7 youth workers). A content analysis (Gibbs, 2008) was performed. The results indicate that the young people used a wide range of new technologies, and this, regardless of their education level. They used digital technologies to learn new things, access cultural products, solve problems, express themselves, organise their social lives, and communicate with friends and family. Another important finding was that the young people’s digital literacy practices cannot be ‘divorced’ from their offline lives (Thomas, 2007); their situation of precarity shaped their online practices. Considering the young people’s financial difficulties, the fact that computers were available on the premises of the two organisations was an appealing element. The organisations were not just offering access to computers and the Internet, but were also supporting young people in learning how to use them. This indicates that even though they were not in education at the time of the study, the young people were still learning about digital literacies. What they learned was directly related to their everyday lives, and in some occasions, countered their situation of precarity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".