“How do we Better Prepare for the Future?”: Political Ambivalence and Income Guarantees in Canadian Media
Bibliographic record
Abstract
Economies in the globalized world are undergoing rapid changes due to automation. These changes have exacerbated wealth inequality in many nations, prompting calls for more effective poverty reduction strategies. In Canada, one of these proposed strategies is an income guarantee for low-income earners. This type of policy has been used successfully in other parts of the world; however, it has been met with both strong support and resistance in Canadian politics. Thus, this opposition provides an avenue through which to study how this debate is framed in the news media to influence public opinion. Through a qualitative content analysis of Canadian newspaper articles, this research demonstrates how the news media employs arguments from both neoliberal and social welfare ideologies in the debate around the viability of an income guarantee. This paper argues that in order to gain traction in a political landscape currently characterized by neoliberalism, supporters of social welfare models must appropriate elements of neoliberal ideologies to produce effective arguments. This appropriation creates an ambivalence for social welfare advocates, as they must incorporate some of these beliefs in order to gain support and enact real change toward poverty reduction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.014 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".