How Large-Scale Social Events Change Employees’ Attitudes and Behaviours
Bibliographic record
Abstract
In this dissertation, I study the relationship between large-scale social events, individual experiences, and social identities. Particularly, I explore the impact of social events, using Quebec's Bill 21 as a case study on people’s experiences and behaviour in the workplace. My discussions and results are based on qualitative and quantitative research. \nI discovered two distinct informants with varying perceptions of Bill 21 in my qualitative study. The individuals directly targeted by the law faced personal, emotional, and daily life consequences and expressed feelings of devaluation, exploitation, exclusion, discrimination, and hopelessness. On the other hand, individuals who did not consider themselves targets maintained a more detached, objective perspective. They focused on societal implications, secularism, neutrality, and the law as a symbol of Quebec's identity and autonomy. \nIn addition, I categorize responses to the challenges of a social event into avoidance behaviours, involving strategies like role change, identity concealment, or enhancement to protect threatened identities, and engagement behaviours encompassing identity distinctiveness, sympathizing among minority groups, and pro-social voice. Avoidance behaviours include individuals leaving their professions or concealing religious symbols, while engagement behaviours involve supporting the law, fostering solidarity among minority groups, and advocating for change. These findings offer insights into the complex dynamics of identity and societal responses, emphasizing the importance of considering social identities and identity threats in understanding reactions to mega-events. \nIn the quantitative study, I study three other overarching mega-events: the MeToo movement, COVID-19, and the change of abortion laws in the US. Additionally, I extend my discussion to include gender and racial identity. By employing both qualitative and quantitative methods, I aim to provide a comprehensive understanding of the impact of social events. While the qualitative study uncovers the nuanced emotional and personal repercussions experienced by those directly targeted by the law, the quantitative phase seeks to understand people’s workplace behaviours in the larger population. \nThis research contributes to understanding mega-events impact on individual identity and behaviour and provides insights into how employees from minority groups react to such events. By shedding light on the relationship between social identities, individual experiences, and mega-events, I offer valuable contributions to both research and practical applications in organizational contexts.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".