Technology of optimization: An emerging configuration of productivity among professional software employees
Bibliographic record
Abstract
In this article, I draw from several months of fieldwork from 2019 to assess professional subjectivity in the software industry of Canada. I assess employees’ constructions of and feelings about their own productivity. I argue that the ways in which subjects understand and feel about their productivity says a great deal about how power is ‘willfully’ negotiated within everyday professional tech settings of neoliberal societies. My findings suggest that optimization is emerging as a technology of self among the individuals I studied, and bringing political consequences. In the first section of the article, I provide a brief overview of the productivity imperative’s cultural trajectory, and show its relation to optimization. Then, in the empirical analysis and discussion, I outline that the technology of optimization involves a discourse around bringing one’s best to public and private realms, offering a specific set of moral ideals. I then show that another facet of this technology of self is centered on willfully entangling public and private life. Finally, I theorize subjects’ reported feelings about their own productivity, assessing how the technology of optimization relates to a politics of privilege. With this study, I seek to make a contribution to the relation between the culture of productivity and professional subjectivity in the software industry, in an effort to expose how power is negotiated at the level of the self in an increasingly influential sector.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".