An Ethnographer’s Exploration of Homeless Shelters’ Performance Measurements
Bibliographic record
Abstract
Utilizing institutional ethnography and a critical analysis, this thesis explicates the textually-mediated process and ruling relations of performance measurement data collection in emergency homeless shelters. The thesis aimed to answer the query of whether the performance measurements collected by a set of programs, within a non-profit social service, adequately captured the full contribution of the work the staff did at their respective emergency shelter. Using literature, that has captured the experiences and insights of frontline workers who feel their work is inadequately captured, as a launch pad, this study spoke to informants who are directly involved in the creation of data collection tools and the reporting of the output and outcome performance measurements. How were these tools created? Who influences the development of the tools? Are some performance indicators (i.e. outputs, quality assurance, outcomes) measured more frequently or thoroughly than others? What are some of the barriers to measuring performance indicators? The study is based on five one-to-one semi-structured interviews, with informants working for a non-profit social service in Southern Ontario, and an analysis of the data collection tools used to compile performance measurements. The purpose of this research is to help social services, especially those that focus on addressing homelessness, improve the tools used to collect statistics on service so as to better articulate the breadth of work done by these services.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".