The agile self: how cultural imperatives in the software sector inform subjectivity
Bibliographic record
Abstract
This dissertation investigates the professional subjectivities of people working in the Canadian software industry, alongside industrial discourses within the sector. It researches the ways in which professionals in software are called to manage themselves and their emotions, and documents how these calls materialize in workplace technologies. It shows how emotion management is compelled by expectations of professional settings, and broader industrial norms, and documents how employees negotiate these expectations and norms. As part of this research a multi-sited ethnography was conducted, including several months of participant observation and interviews at a software company, as well as large-scale conferences and smaller events. The dissertation centers how professional software settings draw from self-improvement discourses, asking what this achieves for organizations and individuals. It shows the ways employees are compelled to understand and manage their inner worlds and exposes how the broader values of the industry are negotiated through subjectivity, and within everyday professional contexts.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.061 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.010 |
| 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".