Bibliographic record
Abstract
Distilling the different ways that neoliberal regimes have impacted the governance of public policy and planning is as much a political maneuvering exercise as it is a scholarly endeavor. The process of empirical research that attempts to uncover such impacts is from the very beginning fraught with numerous problems: sifting through rhetorical ambiguities, dealing with agenda-makers and data gatekeepers, and navigating through complex bureaucratic layers of regulations. These all appear in a concerted effort to maintain and protect the interests of the rational state model. This frustration, although not uncommon in increasingly controlling and risk-adverse regimes, further necessitates that researchers draw on an alternative repertoire of skills and understandings. CitationFlyvbjerg's (2001) argument toward critical social science advocating a phronetic approach—an Aristotelian value-laden concept highlighting the importance of “practical wisdom, practical judgment, common sense and prudence” (Flyvbjerg 2004, 284) balanced by Foucault's power–knowledge relations—provides a useful framework in navigating through these procedural hurdles. Reflecting on the process of research exploring various impacts of the neoliberalization of education in Ontario, this article argues that using a hybrid form of phronesis—engaged in reflexive practices, hidden knowledge and unorthodox sources—combined with the contextual dimensionality of geographic information systems (GIS) is useful to better understand the structural barriers in place and to develop a sensitivity to the political and ethical implications involved in exploring the conflicting spaces of education.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".