Bibliographic record
Abstract
Paul Mees stridently critiqued metropolitan strategic plan-making exercises in cities dominated by neo-liberal governance models (Mees 2003, 2007, 2011). He argued that the noble mission-to design a strategic plan that promotes a vision and a framework to guide planning investment and decisions-is compromised by governance arrangements that uncritically position development outcomes above social and environmental imperatives. Mees' (2000, 2003, 2011') work illuminated the secrecy and 'spin' clouding strategic planning and undercutting rational decision-making in Australian cities. In a period marked by a growing interest in deliberative democracy in planning theory and practice (see Forester 1999; Purcell2009; Hopkins 2010; Elstub 2010), Mees argued for greater democracy and deliberation in strategic plan-making; he looked to what he called 'great planning successes' (Mees 2007, 1110), such as Vancouver, Canada, for inspiration. Comparing metropolitan planning in Melbourne and Vancouver, Mees (2003) was emphatic that Vancouver's Livable Region Strategic Plan (GVRD 1996), delivered seven years before Melbourne's Melbourne 2030 (DI 2002) plan, was 'owned by local governments and community groups [and that this was achieved through] meaningful public participation with rigorous evaluation of alternatives' (Mees 2003, 298).
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 | 0.050 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.066 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".