Context does matter : exploring the influence of moral exculpation in attitudes towards undocumented Mexican migration in Canada
Bibliographic record
Abstract
This thesis examines the role of moral exculpation—a rationale that absolves blame through moral considerations and judgments—in shaping Canadians’ attitudes toward the irregular migration of Mexicans to Canada. The primary aim of this study is to assess whether moral justifications can foster more favorable and permissive attitudes toward this type of migration when the public is exposed to narratives highlighting the economic hardships and violence that compel Mexican migrants to leave their country. To investigate this, the study employs a quantitative survey methodology, engaging 808 respondents who were randomly assigned to three groups: a control group, Treatment 1, and Treatment 2. The study tests two central hypotheses: first, that exposure to a migrant’s personal story can lead to more positive opinions; second, that when individuals are informed of the challenges and adverse conditions faced by migrants, their attitudes will become more permissive. In Treatment 1, participants received a vignette describing a migrant’s departure from Mexico without any moral exculpatory context. In contrast, Treatment 2 included elements suggesting the migrant was forced to leave due to violence or economic hardship. The results indicate a statistically significant increase in favorability among respondents in Treatment 2, supporting the hypothesis that moral considerations can challenge negative perceptions and promote more accepting attitudes toward undocumented migration. These findings contribute to the literature on migration and public opinion by demonstrating the power of moral exculpation to shift public attitudes on highly politicized issues such as irregular migration. The study highlights the importance of moral judgments in influencing public perceptions and suggests that emphasizing the humanitarian challenges migrants face may lead to more favorable attitudes and potentially inform policy debates.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".