Bibliographic record
Abstract
It has been asserted that digital media can improve literacy, engagement, and activism so long as it is promoted and judiciously encouraged by state, political, and societal actors committed to expanding the scope of policy-making to those that otherwise feel ‘left-out'. More specifically, it has been averred that social media, ‘clicktivism,' and electronic referendums have the potential to educate and energize voters on the day-to-day challenges that confront government, and give them a direct say into how certain issues ought to be addressed. However, this chapter argues that while there are still good reasons to be optimistic, looking forward, we also need to critically appraise the false promise(s) of digital media, and do so in a more nuanced fashion. It will be suggested that Canada's comparably low civic literacy rates provide us with some insight into the underlying perils of plebiscitarianism should a more sincere form digital empowerment prevail. It will also be argued that political institutions, culture, Internet usage, populism should also be accounted for.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".