Geosurveillance Through the Mapping of Test Results: An Ethical Dilemma or Public Policy Solution?
Bibliographic record
Abstract
In 1997, the Ontario Provincial Government, through the Education Quality and Accountability Office (EQAO) introduced mandatory standardized testing for grades three and six in Public Elementary Schools as the beginning of a process of public accountability and excellence in education. Proponents for this method of evaluation argued that such procedures were valuable to teachers, schools and the community at large since they would inform teaching and learning. However, in an era of cutback and neoliberal reforms public education like many other public services has increasingly become associated with corporate values related to accountability, efficiency and competition. A review of over 140 articles from 1997 to 2004 reveals a fiercely contested terrain slowly evolving over the years. One of the consequences of these debates was the unintended spatial ramifications linked to neighbourhood identities that were further exacerbated by the way the results were interpreted. For example, discourse analysis demonstrates how the release of test results to the public over the last few years has encouraged an audit culture leading to the labelling of low and high performance schools and to the further social polarization of certain neighbourhoods. In this paper I explore whether the role of Geographical Information Systems (GIS) often criticized as a disciplinary tool could be flipped around and used as an empowering tool instead. To contextualize findings from the discourse analysis, a three-stage GIS placebased approach explores test results taking into account the particularities of each school, civic links and locality characteristics. The context in which these changes (e.g. dismantling of pedagogical infrastructure) unfold reveal the slippages that occur at various scalar levels yet simultaneously considers the ethical implications for the communities involved.
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How this classification was reachedexpand
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".