Creation of the algorithmic management questionnaire: A six‐phase scale development process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract There is an increasing body of research on algorithmic management (AM), but the field lacks measurement tools to capture workers' experiences of this phenomenon. Based on existing literature, we developed and validated the algorithmic management questionnaire (AMQ) to measure the perceptions of workers regarding their level of exposure to AM. Across three samples (overall n = 1332 gig workers), we show the content, factorial, discriminant, convergent, and predictive validity of the scale. The final 20‐item scale assesses workers' perceived level of exposure to algorithmic: monitoring, goal setting, scheduling, performance rating, and compensation. These dimensions formed a higher order construct assessing overall exposure to algorithmic management, which was found to be, as expected, negatively related to the work characteristics of job autonomy and job complexity and, indirectly, to work engagement. Supplementary analyses revealed that perceptions of exposure to AM reflect the objective presence of AM dimensions beyond individual variations in exposure. Overall, the results suggest the suitability of the AMQ to assess workers' perceived exposure to algorithmic management, which paves the way for further research on the impacts of these rapidly accelerating systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it