Plan Canada - Volume 51, Number 1 (Spring 2011)
Bibliographic record
Abstract
Planning for the future now|Le futur de l'urbanisme au présent / Marni Cappe -- CIP news|Échos de l'ICU -- Plan Canada and civility in planning discourse|Plan Canada et la civilité dans le discours des urbanistes / Michael Dudley -- A virtuous circle: How transportation demand management transformed UBC, Vancouver / Andrew E. Jackson -- Comparing Canadian new urbanist and conventional suburban neighbourhoods / Ray Tomalty, Murtaza Haider, & Susan Fisher -- Pathways to climate change resilience: Guiding the adaption of forest-based communities in Canada / Cindy Pearce & Christine Callihoo -- Commercial/retail planning framework for Canadian municipalities / Carol Mak -- Valuing planning: An ethic/ethos in the making? / Ian Wight -- Are we graduating too many planners? / Grant Moore -- [Book review] The geography of aging: Preparing communities for the surge in seniors by Gerald Hodge / Pamela Shaw -- Planning our future: Resilience in a changing world and profession|Le futur de l'urbanisme : La résilience dans us monde et une profession en pleine évolution / Stephanie Chai -- The College of Fellows|Le Collège des Fellows / Robert Lehman -- Reaching out, reaching in: Planners and partners learning in practice|Allons de l'avant tous ensemble : Planificateurs et partenaires apprennent sur le terrain / John Wall
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.273 | 0.049 |
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".