Capitalizing COVID-19: A Content Discourse Analysis of Corporate Welfare Perceptions Amid a Global Pandemic
Bibliographic record
Abstract
Corporate welfare has covertly thrived throughout the COVID-19 pandemic, providing society’s elite with “financial relief” in the form of government subsidies. This method of financial relief is known as the Canada Emergency Wage Subsidy (CEWS), and continues to be used as a method in which corporate welfare transpires. CEWS, a publicly funded benefit initially implemented with the intention to ease businesses back into normal operations, promote their lifespans, prevent additional job losses and re-hire workers, has additionally been used as a means for large, highly solvent corporations to dispense dividends to shareholders and executives amid the economically challenging pandemic of COVID-19. Considering COVID-19 amplified economic insecurity to exceptional levels, questioning and scrutinizing the allocation of tax dollars has been an especially intensified public practice warranting additional research.\nAccording to the Government of Canada, the Canada Emergency Response Benefit has totalled $74.08 billion in CERB payments alone, while the Canada Emergency Wage Subsidy totalled $94.43 billion in approved subsides as of October 3rd, 2021 (Canada Revenue Agency, 2021). Despite the $20.35 billion-dollar difference, the research on Canada’s attitudes towards CERB has far out-paced that on CEWS. To help correct this, this research will seek to reveal how Canadians perceive CEWS and its distribution to large corporations in order to bridge the existing gap between government funded support programs and individual perceptions, as well as contribute to the vast and emerging literature surrounding COVID-19. As such, this research not only intends to uncover and showcase Canadian perceptions towards CEWS and corporate welfare, but to also facilitate discussion regarding the study’s findings while recommending social justice based solutions that stem from the research discoveries.
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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".