Living in Algorithmic Governance: A Study in the Digital Governance of Social Assistance in Ontario
Bibliographic record
Abstract
The aim of this dissertation is to examine the impact of the digital governance of social assistance in Ontario via a new software program, Social Assistance Management System (SAMS).I wanted to know what it feels like to be governed by a software program.This study was designed to address that question.SAMS represents a hybrid of traditional methods of regimenting welfare assistance and a newer high-tech version.I reveal a critical shift in governance practice with SAMS, as the system governs welfare recipients into a particular mode of productive subjectivity in a manner that eliminates the flexibility that was available in previous systems to address specific needs and situations.Despite the wealth and depth of research in surveillance studies about the potential impact of data mining, creation of data doubles, loss of privacy, and concerns about algorithms that replicate some of the worst biases about people in poverty, there is an absence of clear empirical research into or evidence of actual programs and how they work.We need a better understanding of the impact of delegated governance and algorithmic culture in the delivery of social services.Scholars posit that algorithms now govern and dictate the flow of information in many important ways, yet this is often not supported by empirical research and rarely involves interviews or focus groups with people from marginalized communities who are caught up in these algorithms, and without the option to opt out.This dissertation reveals what it feels like to be governed at a distance through a welfare algorithm.To explore this, I interviewed welfare recipients and caseworkers dealing with a software program that has removed much of their professional autonomy and discretion.Drawing from these in-depth interviews, including interviews with two software
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.025 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".