Reproducing disposability: Unsettled labor strategies in the construction of e-commerce markets
Bibliographic record
Abstract
Once the logistics revolution’s paradigmatic retail model, big box retail’s declining growth over the last 15 years has left retailers searching for new outlets of expansion. One solution to this problem has been to construct e-commerce markets in order sell delivery as much as the goods delivered. Similar to Walmart’s ascendance, warehousing is again at the vanguard of these new retail models. Significantly, e-commerce’s demands on warehousing dramatically increase the amount of labor warehouses employ and the quality requirements of warehouse work. This article investigates how warehouse labor is being reproduced and restructured in order to construct e-commerce markets. My research indicates an emerging management strategy to meet these demands by scaling back their reliance on labor market intermediaries. I demonstrate this trend through the examples of two different warehouses where management stopped using temp agencies in one case and reduced their reliance on agencies and third parties in a second case. While these changes moved workers closer to “standard” forms of employment, their disposability was reproduced through discourses of “unskilled” labor and logistical practices of retention. These insights develop our understanding of flexible labor’s production in logistics and challenge broader understandings of labor market intermediaries’ significance.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.045 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".