Technology of optimization: An emerging configuration of productivity among professional software employees
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
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.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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