Fighting poverty : the cases of Portugal and Canada
Bibliographic record
Abstract
Poverty is a widespread global issue that requires immediate attention from all nations. Before the Covid-19 pandemic, several countries actively worked towards addressing this problem, aligned with the United Nations´ SDG. Notably, Canada and Portugal demonstrated their commitment through various initiatives. In 2018, Canadian Prime Minister Justin Trudeau launched the “Opportunity for All” program to reduce poverty by 20% before 2020. Additionally, set an even more ambitious goal to halve poverty levels by 2030 compared to the baseline in 2015. Similarly, Portugal experienced positive results in poverty indicators after a political transition that same year. However, these achievements were unfortunately undermined by the onset of the pandemic. This dissertation examines the specific measures in crucial areas such as healthcare, social support (including family and pension policies) as well as labour market and housing measures implemented by both countries between 2015 and 2020. The outcomes reveal a noteworthy congruence in the measures executed by both countries within these realms, underscoring the paramount importance attributed to alleviating the impact of poverty.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.040 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| 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".