Educational futures after COVID-19: Big tech and pandemic profiteering versus education for democracy
Bibliographic record
Abstract
To address the dramatic economic contraction brought on by the global pandemic, governments at all levels have taken on tremendous debt in order to provide economic stability and prevent a more dramatic collapse. It is likely that, as the initial phase of the pandemic passes, familiar neoliberal austerity claims about the necessity to trim education budgets will gain greater force and acceptance. However, I suggest that these neoliberal policies demand sacrifices of the wrong constituency: Given that Big Tech has amassed huge sums of money over the course of the pandemic, how is it morally justifiable that tech companies benefit from the pandemic while educational institutions shoulder the financial fallout of pandemic government spending? In this paper, I first outline how Big Tech profits from the education sector during the pandemic even as it undermines the democratic function of education in doing so. I then situate these more specific critiques within a broader consideration of the role technology plays in undermining a democratic society. In conclusion, I argue that a pandemic profiteering tax for Big Tech represents the best short-term solution to get ahead of the "austerity curve" and ensure that the COVID-19 crisis serves as an opportunity to deepen our commitments to promoting the democratic function education. Without such commitments, the pandemic will become the turning point at which Big Tech effectively coopts public education for its own ends, to the detriment of democracy. My underlying claim is that technology is in conflict with both democracy and education. This runs against the widespread notion that technology will help promote learning, and that technology helps inform and connect people and therefore helps promote democracy. In what follows I dispel such notions.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".