Digital Technologies’ Transformation of US Store-Based Retail Work: Semi-structured Interviews with Workers and Managers, 2022-2023
Bibliographic record
Abstract
This dataset consists of interview notes on 63 semi-structured, one-hour interviews of store-based retail employees (frontline workers and managers) in various locations in the USA (with the exception of two in Canada), conducted August 2022 through August 2023. The interviews focus on technological change and how it is affecting the labor process. However, they also inquire about the respondents’ career trajectories, pay history, and aspirations for future mobility; the details of their job functions and how those functions are organized; how labor in the store is supervised; any forms of worker collective action; the respondent’s subjective experience of work and supervision; and significant changes in any of these aspects of work. Respondents were recruited via a commercial online interviewee recruitment platform, User Interviews https://www.userinterviews.com/ . The resulting sample is by no means a representative sample of US store-based retail workers. But we believe it is qualitatively representative of more senior employees at larger-unit grocery and general merchandise stores in the US, with a sprinkling of respondents from other types of stores that offer some limited comparisons.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".