“Tax Amazon, Not Working People”: Left Populism and Labor Organizing in Seattle
Bibliographic record
Abstract
Abstract This paper examines the role of labor and community left populist organizing in the Tax Amazon campaign based in Seattle, WA, USA. As a case study within a larger project that examines manifestations of both right-wing and left-wing populism in urban spaces, the paper presents a lens through which urban populism may be examined in relation to new forms of labor organizing, with a specific focus on the dynamics of left populist resistance. The Tax Amazon campaign is situated within a longer trajectory of contemporary left populist organizing in Seattle. Emerging from a previous campaign that resulted in Seattle City Council instituting a $15 minimum wage, the Tax Amazon campaign brought together labor and community organizations to pressure Seattle City Council to introduce a corporate tax to generate funding for affordable housing initiatives. The campaign was met with resistance from the local business community, as well as some local trade unions. Being a leading corporate figure in this opposition, Amazon emerged as a key target of the left populist campaign. Through this study, we ask, what role did labor-community coalitions play in shaping the emergence of a broader left populist politics at the municipal scale in Seattle? Building from the experience of the Tax Amazon campaign, the paper reflects on the dynamics of resistance to corporate power through labor- and community-based left populist organizing. Reflecting a shift in U.S. politics at the municipal scale, Seattle offers a key case through which to assess whether and how urban populism may give rise to new forms of labor organizing. The paper also considers the ways in which the simultaneity of the Tax Amazon campaign and the Movement for Black Lives to ‘Defund the Police’ expanded the scope of political demands in the context of Seattle’s racialized urban precarity. In its conclusion, the paper draws from the analysis to reflect on both the broader prospects and limitations of left populist organising.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".